Author name cluster

Peter Englert

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9 papers

2 author rows

IROS Conference 2018 Conference Paper

Kinematic Morphing Networks for Manipulation Skill Transfer

Peter Englert
Marc Toussaint

The transfer of a robot skill between different geometric environments is non-trivial since a wide variety of environments exists, sensor observations as well as robot motions are high-dimensional, and the environment might only be partially observed. We consider the problem of extracting a low-dimensional description of the manipulated environment in form of a kinematic model. This allows us to transfer a skill by defining a policy on a prototype model and morphing the observed environment to this prototype. A deep neural network is used to map depth image observations of the environment to morphing parameter, which include transformations and configurations of the prototype model. Using the concatenation property of affine transformations and the ability to convert point clouds to depth images allows to apply the network in an iterative manner. The network is trained on data generated in a simulator and on augmented data that is created with its own predictions. The algorithm is evaluated on different tasks, where it is shown that iterative predictions lead to a higher accuracy than one-step predictions.

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IROS Conference 2017 Conference Paper

Active learning with query paths for tactile object shape exploration

Danny Driess
Peter Englert
Marc Toussaint

In the present work, we propose an active learning framework based on optimal query paths to efficiently address the problem of tactile object shape exploration. Most previous approaches perform active touch probing at discrete query points, which leads to inefficient touch-and-retract motions. In contrast, in this paper we propose to query information efficient sliding paths instead of only touch locations. This is realized by three components: A Gaussian process implicit surface model represents the shape and uncertainty of the object. A compliant task/force controller framework fuses the information of this GP model into the parameterization of its tasks, which enables the robot to slide over the unknown object safely and robustly. Thirdly, we develop two strategies to solve the proposed active path querying learning problem. Sliding along those query paths not only creates more dense data than touch probing, but additionally greatly reduces the uncertainty of the object. We demonstrate the effectiveness of our proposed framework both in simulation and on the PR2 robot platform. Furthermore, it is shown that our methodology can be extended to other learning tasks, such as finding a desired surface normal on an unknown object, e. g. for pushing.

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ICRA Conference 2017 Conference Paper

Constrained Bayesian optimization of combined interaction force/task space controllers for manipulations

Danny Driess
Peter Englert
Marc Toussaint

In this paper, we address the problem of how a robot can optimize parameters of combined interaction force/task space controllers under a success constraint in an active way. To enable the robot to explore its environment robustly, safely and without the risk of damaging anything, suitable control concepts have to be developed that enable compliant and force control in situations that are afflicted with high uncertainties. Instances of such concepts are impedance, operational space or hybrid control. However, the parameters of these controllers have to be tuned precisely in order to achieve reasonable performance, which is inherently challenging, as often no sufficient model of the environment is available. To overcome this, we propose to use constrained Bayesian optimization to enable the robot to tune its controller parameters autonomously. Unlike other controller tuning methods, this method allows us to include a success constraint into the optimization. Further, we introduce novel performance measures for compliant, force controlled robots. In real world experiments we show that our approach is able to optimize the parameters for a task that consists of establishing and maintaining contact between the robot and the environment efficiently and successfully.

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IJCAI Conference 2016 Conference Paper

Policy Search in Reproducing Kernel Hilbert Space

Ngo Anh Vien
Peter Englert
Marc Toussaint

Modeling policies in reproducing kernel Hilbert space (RKHS) renders policy gradient reinforcement learning algorithms non-parametric. As a result, the policies become very flexible and have a rich representational potential without a pre-defined set of features. However, their performances might be either non-covariant under re-parameterization of the chosen kernel, or very sensitive to step-size selection. In this paper, we propose to use a general framework to derive a new RKHS policy search technique. The new derivation leads to both a natural RKHS actor-critic algorithm and a RKHS expectation maximization (EM) policy search algorithm. Further, we show that kernelization enables us to learn in partially observable (POMDP) tasks which is considered daunting for parametric approaches. Via sparsification, a small set of "support vectors" representing the history is shown to be effectively discovered. For evaluations, we use three simulated (PO)MDP reinforcement learning tasks, and a simulated PR2's robotic manipulation task. The results demonstrate the effectiveness of the new RKHS policy search framework in comparison to plain RKHS actor-critic, episodic natural actor-critic, plain actor-critic, and PoWER approaches.

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ICRA Conference 2015 Conference Paper

Sparse Gaussian process regression for compliant, real-time robot control

Jens Schreiter
Peter Englert
Duy Nguyen-Tuong
Marc Toussaint

Sparse Gaussian process (GP) models provide an efficient way to perform regression on large data sets. The key idea is to select a representative subset of the available training data, which induces the sparse GP model approximation. In the past, a variety of selection criteria for GP approximation have been proposed, but they either lack accuracy or suffer from high computational costs. In this paper, we introduce a novel and straightforward criterion for successive selection of training points used for GP model approximation. The proposed algorithm allows a fast and efficient selection of training points, while being competitive in learning performance. As evaluation, we employ our approach in learning inverse dynamics models for robot control using very large data sets (e. g. 500. 000 samples). It is demonstrated in experiments that our approximated GP model is sufficiently fast for real-time prediction in robot control. Comparisons with other state-of-the-art approximation techniques show that our proposed approach is significantly faster, while being competitive to generalization accuracy.

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IROS Conference 2014 Conference Paper

Dual execution of optimized contact interaction trajectories

Marc Toussaint
Nathan D. Ratliff
Jeannette Bohg
Ludovic Righetti
Peter Englert
Stefan Schaal

Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept of robustness by funneling: traditional trajectory optimizers do not discover force feedback strategies. From a POMDP perspective, these behaviors could be regarded as explicit observation actions planned to sufficiently reduce uncertainty thereby enabling a task. While we are sympathetic to the full POMDP view, solving full continuous-space POMDPs in high-dimensions is hard. In this paper, we propose an alternative approach in which trajectory optimization objectives are augmented with new terms that reward uncertainty reduction through contacts, explicitly promoting funneling. This augmentation shifts the responsibility of robustness toward the actual execution of the optimized trajectories. Directly tracing trajectories through configuration space would lose all robustness—dual execution achieves robustness by devising force controllers to reproduce the temporal interaction profile encoded in the dual solution of the optimization problem. This work introduces dual execution in depth and analyze its performance through robustness experiments in both simulation and on a real-world robotic platform.

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ICRA Conference 2014 Conference Paper

Multi-task policy search for robotics

Marc Peter Deisenroth
Peter Englert
Jan Peters 0001
Dieter Fox

Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in real-robot experiments are shown.

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IROS Conference 2014 Conference Paper

Reactive phase and task space adaptation for robust motion execution

Peter Englert
Marc Toussaint

An essential aspect for making robots succeed in real-world environments is to give them the ability to robustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that adapts motion plans by feeding changes of the environment into a transformation of the plan in task space. Furthermore, the progress in the plan is defined with a phase variable that is updated adaptively according to the actual task progress. This phase variable releases the strict time compliance that many motion planning methods bring along. The main benefit of our approach is the ability to do this adaptation in a computational efficient manner during the execution of the motion. Thus, the gap between the motion planning and motion execution stage is bridged by continuously transforming geometric and dynamic features of a reference plan to the current situation. We evaluate the performance of our approach by comparing it to alternative methods such as dynamic motion primitives and continuous replanning on several simulated benchmark tasks. Moreover, we demonstrate the real robot applicability on a PR2 robot platform.

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ICRA Conference 2013 Conference Paper

Model-based imitation learning by probabilistic trajectory matching

Peter Englert
Alexandros Paraschos
Jan Peters 0001
Marc Peter Deisenroth

One of the most elegant ways of teaching new skills to robots is to provide demonstrations of a task and let the robot imitate this behavior. Such imitation learning is a non-trivial task: Different anatomies of robot and teacher, and reduced robustness towards changes in the control task are two major difficulties in imitation learning. We present an imitation-learning approach to efficiently learn a task from expert demonstrations. Instead of finding policies indirectly, either via state-action mappings (behavioral cloning), or cost function learning (inverse reinforcement learning), our goal is to find policies directly such that predicted trajectories match observed ones. To achieve this aim, we model the trajectory of the teacher and the predicted robot trajectory by means of probability distributions. We match these distributions by minimizing their Kullback-Leibler divergence. In this paper, we propose to learn probabilistic forward models to compute a probability distribution over trajectories. We compare our approach to model-based reinforcement learning methods with hand-crafted cost functions. Finally, we evaluate our method with experiments on a real compliant robot.

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