Autonomous Motion
Note: This department has relocated.

285 results (View BibTeX file of all listed publications)

2023

Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion
Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion

Dhédin, V., Li, H., Khorshidi, S., Mack, L., Ravi, A. K. C., Meduri, A., Shah, P., Grimminger, F., Righetti, L., Khadiv, M., Stueckler, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2023 (inproceedings)

Abstract
Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.

preprint video link (url) DOI [BibTex]

2023

Dhédin, V., Li, H., Khorshidi, S., Mack, L., Ravi, A. K. C., Meduri, A., Shah, P., Grimminger, F., Righetti, L., Khadiv, M., Stueckler, J. Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2023 (inproceedings)

preprint video link (url) DOI [BibTex]

2021

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Differentiable Factor Graph Optimization for Learning Smoothers

Yi, B. L. M. A. K. A. M. R. B. J.

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1339-1345, IEEE, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), September 2021 (conference)

DOI [BibTex]

2021

Yi, B. L. M. A. K. A. M. R. B. J. Differentiable Factor Graph Optimization for Learning Smoothers 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1339-1345, IEEE, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), September 2021 (conference)

DOI [BibTex]

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Implementation of a Reactive Walking Controller for the New Open-Hardware Quadruped Solo-12

Leziart, P. F. T. G. F. M. N. S. P.

Proceedings IEEE International Conference on Robotics and Automation (ICRA), pages: 5007-5013, IEEE, IEEE International Conference on Robotics and Automation (ICRA), June 2021 (conference)

DOI [BibTex]

Leziart, P. F. T. G. F. M. N. S. P. Implementation of a Reactive Walking Controller for the New Open-Hardware Quadruped Solo-12 Proceedings IEEE International Conference on Robotics and Automation (ICRA), pages: 5007-5013, IEEE, IEEE International Conference on Robotics and Automation (ICRA), June 2021 (conference)

DOI [BibTex]

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GraspME - Grasp Manifold Estimator

Hager, J., Bauer, R., Toussaint, M., Mainprice, J.

In 2021 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), pages: 626-632, IEEE, Piscataway, NJ, 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021) , 2021 (inproceedings)

DOI [BibTex]

Hager, J., Bauer, R., Toussaint, M., Mainprice, J. GraspME - Grasp Manifold Estimator In 2021 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), pages: 626-632, IEEE, Piscataway, NJ, 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021) , 2021 (inproceedings)

DOI [BibTex]

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A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures

Oh, Y., Schäfer, T., Rüther, B., Toussaint, M., Mainprice, J.

In 2021 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), pages: 503-508, IEEE, Piscataway, NJ, 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), 2021 (inproceedings)

DOI [BibTex]

Oh, Y., Schäfer, T., Rüther, B., Toussaint, M., Mainprice, J. A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures In 2021 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), pages: 503-508, IEEE, Piscataway, NJ, 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), 2021 (inproceedings)

DOI [BibTex]

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Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks

Le, A. T., Kratzer, P., Hagenmayer, S., Toussaint, M., Mainprice, J.

In 2021 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021) , pages: 7-14, IEEE, Piscataway, NJ, 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), 2021 (inproceedings)

DOI [BibTex]

Le, A. T., Kratzer, P., Hagenmayer, S., Toussaint, M., Mainprice, J. Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks In 2021 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021) , pages: 7-14, IEEE, Piscataway, NJ, 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2021), 2021 (inproceedings)

DOI [BibTex]

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Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies

Oh, Y., Toussaint, M., Mainprice, J.

In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), pages: 5305-5311, IEEE, Piscataway, NJ, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), 2021 (inproceedings)

DOI [BibTex]

Oh, Y., Toussaint, M., Mainprice, J. Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), pages: 5305-5311, IEEE, Piscataway, NJ, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), 2021 (inproceedings)

DOI [BibTex]

2020

TriFinger: An Open-Source Robot for Learning Dexterity
TriFinger: An Open-Source Robot for Learning Dexterity

Wüthrich, M., Widmaier, F., Grimminger, F., Akpo, J., Joshi, S., Agrawal, V., Hammoud, B., Khadiv, M., Bogdanovic, M., Berenz, V., Viereck, J., Naveau, M., Righetti, L., Schölkopf, B., Bauer, S.

Proceedings of the 4th Conference on Robot Learning (CoRL), 155, pages: 1871-1882, Proceedings of Machine Learning Research, (Editors: Jens Kober and Fabio Ramos and Claire J. Tomlin), PMLR, November 2020 (conference)

PDF link (url) [BibTex]

2020

Wüthrich, M., Widmaier, F., Grimminger, F., Akpo, J., Joshi, S., Agrawal, V., Hammoud, B., Khadiv, M., Bogdanovic, M., Berenz, V., Viereck, J., Naveau, M., Righetti, L., Schölkopf, B., Bauer, S. TriFinger: An Open-Source Robot for Learning Dexterity Proceedings of the 4th Conference on Robot Learning (CoRL), 155, pages: 1871-1882, Proceedings of Machine Learning Research, (Editors: Jens Kober and Fabio Ramos and Claire J. Tomlin), PMLR, November 2020 (conference)

PDF link (url) [BibTex]

Learning Sensory-Motor Associations from Demonstration
Learning Sensory-Motor Associations from Demonstration

Berenz, V., Bjelic, A., Herath, L., Mainprice, J.

29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference)

Abstract
We propose a method which generates reactive robot behavior learned from human demonstration. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. This allows us to represent the learned behavior as a set of associations between sensor and motor primitives in a human readable script. Distinguishing between sensor and motor primitives introduces a supplementary level of granularity and more importantly enforces feedback, increasing adaptability and robustness. As the experimental section shows, useful behaviors may be learned from a single demonstration covering a very limited portion of the task space.

[BibTex]

Berenz, V., Bjelic, A., Herath, L., Mainprice, J. Learning Sensory-Motor Associations from Demonstration 29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference)

[BibTex]

Accurate Vision-based Manipulation through Contact Reasoning
Accurate Vision-based Manipulation through Contact Reasoning

Kloss, A., Bauza, M., Wu, J., Tenenbaum, J. B., Rodriguez, A., Bohg, J.

In International Conference on Robotics and Automation, May 2020 (inproceedings) Accepted

Abstract
Planning contact interactions is one of the core challenges of many robotic tasks. Optimizing contact locations while taking dynamics into account is computationally costly and in only partially observed environments, executing contact-based tasks often suffers from low accuracy. We present an approach that addresses these two challenges for the problem of vision-based manipulation. First, we propose to disentangle contact from motion optimization. Thereby, we improve planning efficiency by focusing computation on promising contact locations. Second, we use a hybrid approach for perception and state estimation that combines neural networks with a physically meaningful state representation. In simulation and real-world experiments on the task of planar pushing, we show that our method is more efficient and achieves a higher manipulation accuracy than previous vision-based approaches.

Video link (url) [BibTex]

Kloss, A., Bauza, M., Wu, J., Tenenbaum, J. B., Rodriguez, A., Bohg, J. Accurate Vision-based Manipulation through Contact Reasoning In International Conference on Robotics and Automation, May 2020 (inproceedings) Accepted

Video link (url) [BibTex]

Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

Marco, A., Rohr, A. V., Baumann, D., Hernández-Lobato, J. M., Trimpe, S.

2020 (proceedings) In revision

Abstract
When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.

arXiv code (python) PDF [BibTex]

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A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Agudelo-España, D., Zadaianchuk, A., Wenk, P., Garg, A., Akpo, J., Grimminger, F., Viereck, J., Naveau, M., Righetti, L., Martius, G., Krause, A., Schölkopf, B., Bauer, S., Wüthrich, M.

IEEE International Conference on Robotics and Automation (ICRA), pages: 8151-8157, IEEE, 2020 (conference)

Project Page PDF DOI Project Page [BibTex]

Agudelo-España, D., Zadaianchuk, A., Wenk, P., Garg, A., Akpo, J., Grimminger, F., Viereck, J., Naveau, M., Righetti, L., Martius, G., Krause, A., Schölkopf, B., Bauer, S., Wüthrich, M. A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models IEEE International Conference on Robotics and Automation (ICRA), pages: 8151-8157, IEEE, 2020 (conference)

Project Page PDF DOI Project Page [BibTex]

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An Interior Point Method Solving Motion Planning Problems with Narrow Passages

Mainprice, J., Ratliff, N., Toussaint, M., Schaal, S.

In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), pages: 547-552, IEEE, Piscataway, NJ, 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), 2020 (inproceedings)

DOI [BibTex]

Mainprice, J., Ratliff, N., Toussaint, M., Schaal, S. An Interior Point Method Solving Motion Planning Problems with Narrow Passages In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), pages: 547-552, IEEE, Piscataway, NJ, 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), 2020 (inproceedings)

DOI [BibTex]

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Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks

Kratzer, P., Toussaint, M., Mainprice, J.

In 2020 IEEE International Conference on Robotics and Automation (ICRA 2020), pages: 1792-1798, IEEE, Piscataway, NJ, IEEE International Conference on Robotics and Automation (ICRA 2020), 2020 (inproceedings)

DOI [BibTex]

Kratzer, P., Toussaint, M., Mainprice, J. Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks In 2020 IEEE International Conference on Robotics and Automation (ICRA 2020), pages: 1792-1798, IEEE, Piscataway, NJ, IEEE International Conference on Robotics and Automation (ICRA 2020), 2020 (inproceedings)

DOI [BibTex]

2019

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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Gondal, M. W., Wüthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., Bauer, S.

Advances in Neural Information Processing Systems 32 (NeurIPS 2019), pages: 15714-15725, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) Project Page [BibTex]

2019

Gondal, M. W., Wüthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., Bauer, S. On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset Advances in Neural Information Processing Systems 32 (NeurIPS 2019), pages: 15714-15725, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

link (url) Project Page [BibTex]

Learning Latent Space Dynamics for Tactile Servoing
Learning Latent Space Dynamics for Tactile Servoing

Sutanto, G., Ratliff, N., Sundaralingam, B., Chebotar, Y., Su, Z., Handa, A., Fox, D.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019, IEEE, International Conference on Robotics and Automation, May 2019 (inproceedings) Accepted

pdf video [BibTex]

Sutanto, G., Ratliff, N., Sundaralingam, B., Chebotar, Y., Su, Z., Handa, A., Fox, D. Learning Latent Space Dynamics for Tactile Servoing In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019, IEEE, International Conference on Robotics and Automation, May 2019 (inproceedings) Accepted

pdf video [BibTex]

Leveraging Contact Forces for Learning to Grasp
Leveraging Contact Forces for Learning to Grasp

Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019, IEEE, International Conference on Robotics and Automation, May 2019 (inproceedings)

Abstract
Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two- fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.

video arXiv [BibTex]

Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J. Leveraging Contact Forces for Learning to Grasp In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019, IEEE, International Conference on Robotics and Automation, May 2019 (inproceedings)

video arXiv [BibTex]

2018

Probabilistic Recurrent State-Space Models
Probabilistic Recurrent State-Space Models

Doerr, A., Daniel, C., Schiegg, M., Nguyen-Tuong, D., Schaal, S., Toussaint, M., Trimpe, S.

In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), July 2018 (inproceedings)

Abstract
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex time-series data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalable initialization and training algorithm based on doubly stochastic variational inference and Gaussian processes. In the variational approximation we propose in contrast to related approaches to fully capture the latent state temporal correlations to allow for robust training.

arXiv pdf Project Page [BibTex]

2018

Doerr, A., Daniel, C., Schiegg, M., Nguyen-Tuong, D., Schaal, S., Toussaint, M., Trimpe, S. Probabilistic Recurrent State-Space Models In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), July 2018 (inproceedings)

arXiv pdf Project Page [BibTex]

Online Learning of a Memory for Learning Rates
Online Learning of a Memory for Learning Rates

(nominated for best paper award)

Meier, F., Kappler, D., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018, accepted (inproceedings)

Abstract
The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks. Here, we introduce a computationally efficient online meta-learning algorithm that builds and optimizes a memory model of the optimal learning rate landscape from previously observed gradient behaviors. While performing task specific optimization, this memory of learning rates predicts how to scale currently observed gradients. After applying the gradient scaling our meta-learner updates its internal memory based on the observed effect its prediction had. Our meta-learner can be combined with any gradient-based optimizer, learns on the fly and can be transferred to new optimization tasks. In our evaluations we show that our meta-learning algorithm speeds up learning of MNIST classification and a variety of learning control tasks, either in batch or online learning settings.

pdf video code [BibTex]

Meier, F., Kappler, D., Schaal, S. Online Learning of a Memory for Learning Rates In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018, accepted (inproceedings)

pdf video code [BibTex]

Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks

Sutanto, G., Su, Z., Schaal, S., Meier, F.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

pdf video [BibTex]

Sutanto, G., Su, Z., Schaal, S., Meier, F. Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

pdf video [BibTex]

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On Time Optimization of Centroidal Momentum Dynamics

Ponton, B., Herzog, A., Del Prete, A., Schaal, S., Righetti, L.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 5776-5782, IEEE, Brisbane, Australia, 2018 (inproceedings)

Abstract
Recently, the centroidal momentum dynamics has received substantial attention to plan dynamically consistent motions for robots with arms and legs in multi-contact scenarios. However, it is also non convex which renders any optimization approach difficult and timing is usually kept fixed in most trajectory optimization techniques to not introduce additional non convexities to the problem. But this can limit the versatility of the algorithms. In our previous work, we proposed a convex relaxation of the problem that allowed to efficiently compute momentum trajectories and contact forces. However, our approach could not minimize a desired angular momentum objective which seriously limited its applicability. Noticing that the non-convexity introduced by the time variables is of similar nature as the centroidal dynamics one, we propose two convex relaxations to the problem based on trust regions and soft constraints. The resulting approaches can compute time-optimized dynamically consistent trajectories sufficiently fast to make the approach realtime capable. The performance of the algorithm is demonstrated in several multi-contact scenarios for a humanoid robot. In particular, we show that the proposed convex relaxation of the original problem finds solutions that are consistent with the original non-convex problem and illustrate how timing optimization allows to find motion plans that would be difficult to plan with fixed timing † †Implementation details and demos can be found in the source code available at https://git-amd.tuebingen.mpg.de/bponton/timeoptimization.

link (url) DOI [BibTex]

Ponton, B., Herzog, A., Del Prete, A., Schaal, S., Righetti, L. On Time Optimization of Centroidal Momentum Dynamics In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 5776-5782, IEEE, Brisbane, Australia, 2018 (inproceedings)

link (url) DOI [BibTex]

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Unsupervised Contact Learning for Humanoid Estimation and Control

Rotella, N., Schaal, S., Righetti, L.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 411-417, IEEE, Brisbane, Australia, 2018 (inproceedings)

Abstract
This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.

link (url) DOI [BibTex]

Rotella, N., Schaal, S., Righetti, L. Unsupervised Contact Learning for Humanoid Estimation and Control In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 411-417, IEEE, Brisbane, Australia, 2018 (inproceedings)

link (url) DOI [BibTex]

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Learning Task-Specific Dynamics to Improve Whole-Body Control

Gams, A., Mason, S., Ude, A., Schaal, S., Righetti, L.

In Hua, IEEE, Beijing, China, November 2018 (inproceedings)

Abstract
In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, high feedback terms can be used to improve tracking accuracy; however, this can lead to very stiff behavior or poor tracking accuracy due to limited control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. With a systematic approach we also reduce heuristic tuning of the model parameters and feedback gains, often present in real-world experiments. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach.

link (url) [BibTex]

Gams, A., Mason, S., Ude, A., Schaal, S., Righetti, L. Learning Task-Specific Dynamics to Improve Whole-Body Control In Hua, IEEE, Beijing, China, November 2018 (inproceedings)

link (url) [BibTex]

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An MPC Walking Framework With External Contact Forces

Mason, S., Rotella, N., Schaal, S., Righetti, L.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1785-1790, IEEE, Brisbane, Australia, May 2018 (inproceedings)

Abstract
In this work, we present an extension to a linear Model Predictive Control (MPC) scheme that plans external contact forces for the robot when given multiple contact locations and their corresponding friction cone. To this end, we set up a two-step optimization problem. In the first optimization, we compute the Center of Mass (CoM) trajectory, foot step locations, and introduce slack variables to account for violating the imposed constraints on the Zero Moment Point (ZMP). We then use the slack variables to trigger the second optimization, in which we calculate the optimal external force that compensates for the ZMP tracking error. This optimization considers multiple contacts positions within the environment by formulating the problem as a Mixed Integer Quadratic Program (MIQP) that can be solved at a speed between 100-300 Hz. Once contact is created, the MIQP reduces to a single Quadratic Program (QP) that can be solved in real-time ({\textless}; 1kHz). Simulations show that the presented walking control scheme can withstand disturbances 2-3× larger with the additional force provided by a hand contact.

link (url) DOI [BibTex]

Mason, S., Rotella, N., Schaal, S., Righetti, L. An MPC Walking Framework With External Contact Forces In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1785-1790, IEEE, Brisbane, Australia, May 2018 (inproceedings)

link (url) DOI [BibTex]

2017

Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

Hausman, K., Chebotar, Y., Schaal, S., Sukhatme, G., Lim, J.

In Proceedings from the conference "Neural Information Processing Systems 2017., (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (inproceedings)

pdf video [BibTex]

2017

Hausman, K., Chebotar, Y., Schaal, S., Sukhatme, G., Lim, J. Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets In Proceedings from the conference "Neural Information Processing Systems 2017., (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (inproceedings)

pdf video [BibTex]

On the Design of {LQR} Kernels for Efficient Controller Learning
On the Design of LQR Kernels for Efficient Controller Learning

Marco, A., Hennig, P., Schaal, S., Trimpe, S.

Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC), pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

Abstract
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page Project Page [BibTex]

Marco, A., Hennig, P., Schaal, S., Trimpe, S. On the Design of LQR Kernels for Efficient Controller Learning Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC), pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page Project Page [BibTex]

Optimizing Long-term Predictions for Model-based Policy Search
Optimizing Long-term Predictions for Model-based Policy Search

Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., Trimpe, S.

Proceedings of 1st Annual Conference on Robot Learning (CoRL), 78, pages: 227-238, (Editors: Sergey Levine and Vincent Vanhoucke and Ken Goldberg), 1st Annual Conference on Robot Learning, November 2017 (conference)

Abstract
We propose a novel long-term optimization criterion to improve the robustness of model-based reinforcement learning in real-world scenarios. Learning a dynamics model to derive a solution promises much greater data-efficiency and reusability compared to model-free alternatives. In practice, however, modelbased RL suffers from various imperfections such as noisy input and output data, delays and unmeasured (latent) states. To achieve higher resilience against such effects, we propose to optimize a generative long-term prediction model directly with respect to the likelihood of observed trajectories as opposed to the common approach of optimizing a dynamics model for one-step-ahead predictions. We evaluate the proposed method on several artificial and real-world benchmark problems and compare it to PILCO, a model-based RL framework, in experiments on a manipulation robot. The results show that the proposed method is competitive compared to state-of-the-art model learning methods. In contrast to these more involved models, our model can directly be employed for policy search and outperforms a baseline method in the robot experiment.

PDF Project Page [BibTex]

Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., Trimpe, S. Optimizing Long-term Predictions for Model-based Policy Search Proceedings of 1st Annual Conference on Robot Learning (CoRL), 78, pages: 227-238, (Editors: Sergey Levine and Vincent Vanhoucke and Ken Goldberg), 1st Annual Conference on Robot Learning, November 2017 (conference)

PDF Project Page [BibTex]

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A New Data Source for Inverse Dynamics Learning

Kappler, D., Meier, F., Ratliff, N., Schaal, S.

In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017 (inproceedings)

[BibTex]

Kappler, D., Meier, F., Ratliff, N., Schaal, S. A New Data Source for Inverse Dynamics Learning In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017 (inproceedings)

[BibTex]

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Bayesian Regression for Artifact Correction in Electroencephalography

Fiebig, K., Jayaram, V., Hesse, T., Blank, A., Peters, J., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 131-136, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

DOI [BibTex]

Fiebig, K., Jayaram, V., Hesse, T., Blank, A., Peters, J., Grosse-Wentrup, M. Bayesian Regression for Artifact Correction in Electroencephalography Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 131-136, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

DOI [BibTex]

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Investigating Music Imagery as a Cognitive Paradigm for Low-Cost Brain-Computer Interfaces

Grossberger, L., Hohmann, M. R., Peters, J., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 160-164, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

DOI [BibTex]

Grossberger, L., Hohmann, M. R., Peters, J., Grosse-Wentrup, M. Investigating Music Imagery as a Cognitive Paradigm for Low-Cost Brain-Computer Interfaces Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 160-164, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

DOI [BibTex]

On the relevance of grasp metrics for predicting grasp success
On the relevance of grasp metrics for predicting grasp success

Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J.

In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems, September 2017 (inproceedings) Accepted

Abstract
We aim to reliably predict whether a grasp on a known object is successful before it is executed in the real world. There is an entire suite of grasp metrics that has already been developed which rely on precisely known contact points between object and hand. However, it remains unclear whether and how they may be combined into a general purpose grasp stability predictor. In this paper, we analyze these questions by leveraging a large scale database of simulated grasps on a wide variety of objects. For each grasp, we compute the value of seven metrics. Each grasp is annotated by human subjects with ground truth stability labels. Given this data set, we train several classification methods to find out whether there is some underlying, non-trivial structure in the data that is difficult to model manually but can be learned. Quantitative and qualitative results show the complexity of the prediction problem. We found that a good prediction performance critically depends on using a combination of metrics as input features. Furthermore, non-parametric and non-linear classifiers best capture the structure in the data.

Project Page [BibTex]

Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J. On the relevance of grasp metrics for predicting grasp success In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems, September 2017 (inproceedings) Accepted

Project Page [BibTex]

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Local Bayesian Optimization of Motor Skills

Akrour, R., Sorokin, D., Peters, J., Neumann, G.

Proceedings of the 34th International Conference on Machine Learning (ICML), 70, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, August 2017 (conference)

link (url) [BibTex]

Akrour, R., Sorokin, D., Peters, J., Neumann, G. Local Bayesian Optimization of Motor Skills Proceedings of the 34th International Conference on Machine Learning (ICML), 70, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, August 2017 (conference)

link (url) [BibTex]

Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

Chebotar, Y., Hausman, K., Zhang, M., Sukhatme, G., Schaal, S., Levine, S.

Proceedings of the 34th International Conference on Machine Learning, 70, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (conference)

pdf video [BibTex]

Chebotar, Y., Hausman, K., Zhang, M., Sukhatme, G., Schaal, S., Levine, S. Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning Proceedings of the 34th International Conference on Machine Learning, 70, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (conference)

pdf video [BibTex]

Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 5295-5301, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

PDF arXiv DOI Project Page [BibTex]

Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., Trimpe, S. Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 5295-5301, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

PDF arXiv DOI Project Page [BibTex]

Learning Feedback Terms for Reactive Planning and Control
Learning Feedback Terms for Reactive Planning and Control

Rai, A., Sutanto, G., Schaal, S., Meier, F.

Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (conference)

pdf video [BibTex]

Rai, A., Sutanto, G., Schaal, S., Meier, F. Learning Feedback Terms for Reactive Planning and Control Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (conference)

pdf video [BibTex]

Virtual vs. {R}eal: Trading Off Simulations and Physical Experiments in Reinforcement Learning with {B}ayesian Optimization
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 1557-1563, IEEE, Piscataway, NJ, USA, May 2017 (inproceedings)

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page Project Page [BibTex]

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S. Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 1557-1563, IEEE, Piscataway, NJ, USA, May 2017 (inproceedings)

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page Project Page [BibTex]

2016

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Predictive and Self Triggering for Event-based State Estimation

Trimpe, S.

In Proceedings of the 55th IEEE Conference on Decision and Control (CDC), pages: 3098-3105, Las Vegas, NV, USA, December 2016 (inproceedings)

arXiv PDF DOI Project Page [BibTex]

2016

Trimpe, S. Predictive and Self Triggering for Event-based State Estimation In Proceedings of the 55th IEEE Conference on Decision and Control (CDC), pages: 3098-3105, Las Vegas, NV, USA, December 2016 (inproceedings)

arXiv PDF DOI Project Page [BibTex]

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Using Probabilistic Movement Primitives for Striking Movements

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 502-508, November 2016 (conference)

link (url) DOI [BibTex]

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J. Using Probabilistic Movement Primitives for Striking Movements 16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 502-508, November 2016 (conference)

link (url) DOI [BibTex]

Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis
Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 650-655, November 2016 (conference)

final link (url) DOI [BibTex]

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J. Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis 16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 650-655, November 2016 (conference)

final link (url) DOI [BibTex]

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The Role of Measurement Uncertainty in Optimal Control for Contact Interactions
Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)

Abstract
Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications that involve interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of pre- cise knowledge of the world, which is not an actual disturbance. We de- velop a computationally efficient SOC algorithm, based on risk-sensitive control, that takes into account uncertainty in the measurements. We include the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. We show that high measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise variance that creates stiff behaviors. Simulation results on a simple 2D manipulator show that our controller can create better interaction with the environment under uncertain contact locations than traditional SOC approaches.

arXiv [BibTex]

The Role of Measurement Uncertainty in Optimal Control for Contact Interactions Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)

arXiv [BibTex]

Learning Where to Search Using Visual Attention
Learning Where to Search Using Visual Attention

Kloss, A., Kappler, D., Lensch, H. P. A., Butz, M. V., Schaal, S., Bohg, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)

Abstract
One of the central tasks for a household robot is searching for specific objects. It does not only require localizing the target object but also identifying promising search locations in the scene if the target is not immediately visible. As computation time and hardware resources are usually limited in robotics, it is desirable to avoid expensive visual processing steps that are exhaustively applied over the entire image. The human visual system can quickly select those image locations that have to be processed in detail for a given task. This allows us to cope with huge amounts of information and to efficiently deploy the limited capacities of our visual system. In this paper, we therefore propose to use human fixation data to train a top-down saliency model that predicts relevant image locations when searching for specific objects. We show that the learned model can successfully prune bounding box proposals without rejecting the ground truth object locations. In this aspect, the proposed model outperforms a model that is trained only on the ground truth segmentations of the target object instead of fixation data.

Project Page [BibTex]

Kloss, A., Kappler, D., Lensch, H. P. A., Butz, M. V., Schaal, S., Bohg, J. Learning Where to Search Using Visual Attention Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)

PDF Project Page [BibTex]

Parameter Learning for Improving Binary Descriptor Matching
Parameter Learning for Improving Binary Descriptor Matching

Sankaran, B., Ramalingam, S., Taguchi, Y.

In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2016 (inproceedings)

Abstract
Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and is comparable to SIFT.

[BibTex]

Sankaran, B., Ramalingam, S., Taguchi, Y. Parameter Learning for Improving Binary Descriptor Matching In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2016 (inproceedings)

[BibTex]

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A New Trajectory Generation Framework in Robotic Table Tennis

Koc, O., Maeda, G., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pages: 3750-3756, October 2016 (conference)

link (url) DOI [BibTex]

Koc, O., Maeda, G., Peters, J. A New Trajectory Generation Framework in Robotic Table Tennis Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pages: 3750-3756, October 2016 (conference)

link (url) DOI [BibTex]

Superpixel Convolutional Networks using Bilateral Inceptions
Superpixel Convolutional Networks using Bilateral Inceptions

Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, Springer, 14th European Conference on Computer Vision, October 2016 (inproceedings)

Abstract
In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new “bilateral inception” module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception modules between the last CNN (1 × 1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.

pdf supplementary poster Project Page Project Page [BibTex]

Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P. Superpixel Convolutional Networks using Bilateral Inceptions In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, Springer, 14th European Conference on Computer Vision, October 2016 (inproceedings)

pdf supplementary poster Project Page Project Page [BibTex]

Barrista - Caffe Well-Served
Barrista - Caffe Well-Served

Lassner, C., Kappler, D., Kiefel, M., Gehler, P.

In ACM Multimedia Open Source Software Competition, ACM OSSC16, October 2016 (inproceedings)

Abstract
The caffe framework is one of the leading deep learning toolboxes in the machine learning and computer vision community. While it offers efficiency and configurability, it falls short of a full interface to Python. With increasingly involved procedures for training deep networks and reaching depths of hundreds of layers, creating configuration files and keeping them consistent becomes an error prone process. We introduce the barrista framework, offering full, pythonic control over caffe. It separates responsibilities and offers code to solve frequently occurring tasks for pre-processing, training and model inspection. It is compatible to all caffe versions since mid 2015 and can import and export .prototxt files. Examples are included, e.g., a deep residual network implemented in only 172 lines (for arbitrary depths), comparing to 2320 lines in the official implementation for the equivalent model.

pdf link (url) DOI Project Page [BibTex]

Lassner, C., Kappler, D., Kiefel, M., Gehler, P. Barrista - Caffe Well-Served In ACM Multimedia Open Source Software Competition, ACM OSSC16, October 2016 (inproceedings)

pdf link (url) DOI Project Page [BibTex]

Robust Gaussian Filtering using a Pseudo Measurement
Robust Gaussian Filtering using a Pseudo Measurement

Wüthrich, M., Garcia Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S.

In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)

Abstract
Most widely-used state estimation algorithms, such as the Extended Kalman Filter and the Unscented Kalman Filter, belong to the family of Gaussian Filters (GF). Unfortunately, GFs fail if the measurement process is modelled by a fat-tailed distribution. This is a severe limitation, because thin-tailed measurement models, such as the analytically-convenient and therefore widely-used Gaussian distribution, are sensitive to outliers. In this paper, we show that mapping the measurements into a specific feature space enables any existing GF algorithm to work with fat-tailed measurement models. We find a feature function which is optimal under certain conditions. Simulation results show that the proposed method allows for robust filtering in both linear and nonlinear systems with measurements contaminated by fat-tailed noise.

Web link (url) DOI Project Page [BibTex]

Wüthrich, M., Garcia Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S. Robust Gaussian Filtering using a Pseudo Measurement In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)

Web link (url) DOI Project Page [BibTex]

Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles
Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles

Widmaier, F., Kappler, D., Schaal, S., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
To achieve accurate vision-based control with a robotic arm, a good hand-eye coordination is required. However, knowing the current configuration of the arm can be very difficult due to noisy readings from joint encoders or an inaccurate hand-eye calibration. We propose an approach for robot arm pose estimation that uses depth images of the arm as input to directly estimate angular joint positions. This is a frame-by-frame method which does not rely on good initialisation of the solution from the previous frames or knowledge from the joint encoders. For estimation, we employ a random regression forest which is trained on synthetically generated data. We compare different training objectives of the forest and also analyse the influence of prior segmentation of the arms on accuracy. We show that this approach improves previous work both in terms of computational complexity and accuracy. Despite being trained on synthetic data only, we demonstrate that the estimation also works on real depth images.

pdf DOI Project Page [BibTex]

Widmaier, F., Kappler, D., Schaal, S., Bohg, J. Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

pdf DOI Project Page [BibTex]

Optimizing for what matters: the Top Grasp Hypothesis
Optimizing for what matters: the Top Grasp Hypothesis

Kappler, D., Schaal, S., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
In this paper, we consider the problem of robotic grasping of objects when only partial and noisy sensor data of the environment is available. We are specifically interested in the problem of reliably selecting the best hypothesis from a whole set. This is commonly the case when trying to grasp an object for which we can only observe a partial point cloud from one viewpoint through noisy sensors. There will be many possible ways to successfully grasp this object, and even more which will fail. We propose a supervised learning method that is trained with a ranking loss. This explicitly encourages that the top-ranked training grasp in a hypothesis set is also positively labeled. We show how we adapt the standard ranking loss to work with data that has binary labels and explain the benefits of this formulation. Additionally, we show how we can efficiently optimize this loss with stochastic gradient descent. In quantitative experiments, we show that we can outperform previous models by a large margin.

pdf DOI Project Page [BibTex]

Kappler, D., Schaal, S., Bohg, J. Optimizing for what matters: the Top Grasp Hypothesis In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

pdf DOI Project Page [BibTex]

Exemplar-based Prediction of Object Properties from Local Shape Similarity
Exemplar-based Prediction of Object Properties from Local Shape Similarity

Bohg, J., Kappler, D., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
We propose a novel method that enables a robot to identify a graspable object part of an unknown object given only noisy and partial information that is obtained from an RGB-D camera. Our method combines the benefits of local with the advantages of global methods. It learns a classifier that takes a local shape representation as input and outputs the probability that a grasp applied at this location will be successful. Given a query data point that is classified in this way, we can retrieve all the locally similar training data points and use them to predict latent global object shape. This information may help to further prune positively labeled grasp hypotheses based on, e.g. relation to the predicted average global shape or suitability for a specific task. This prediction can also guide scene exploration to prune object shape hypotheses. To learn the function that maps local shape to grasp stability we use a Random Forest Classifier. We show that our method reaches the same classification performance as the current state-of-the-art on this dataset which uses a Convolutional Neural Network. Additionally, we exploit the natural ability of the Random Forest to cluster similar data. For a positively predicted query data point, we retrieve all the locally similar training data points that are associated with the same leaf nodes of the Random Forest. The main insight from this work is that local object shape that affords a grasp is also a good predictor of global object shape. We empirically support this claim with quantitative experiments. Additionally, we demonstrate the predictive capability of the method on some real data examples.

pdf DOI Project Page [BibTex]

Bohg, J., Kappler, D., Schaal, S. Exemplar-based Prediction of Object Properties from Local Shape Similarity In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

pdf DOI Project Page [BibTex]