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Meta-learning Regrasping Strategies for Physical-Agnostic Objects

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arXiv 2022年

作者： Gao, Ning Zhang, Jingyu Chen, Ruijie Vien, Ngo Anh Ziesche, Hanna Neumann, Gerhard Bosch Center for Artificial Intelligence Renningen Germany Autonomous Learning Robots Lab Karlsruhe Institute of Technology Karlsruhe Germany

Grasping inhomogeneous objects in real-world applications remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction. In this study, we propose a meta-learning algorithm called ConDex, which incorporates Conditional Neural Processes (CNP) with DexNet-2.0 to autonomously discern the underlying physical properties of objects using depth images. ConDex efficiently acquires physical embeddings from limited trials, enabling precise grasping point estimation. Furthermore, ConDex is capable of updating the predicted grasping quality iteratively from new trials in an online fashion. To the best of our knowledge, we are the first who generate two object datasets focusing on inhomogeneous physical properties with varying mass distributions and friction coefficients. Extensive evaluations in simulation demonstrate ConDex’s superior performance over DexNet-2.0 and existing meta-learning-based grasping pipelines. Furthermore, ConDex shows robust generalization to previously unseen real-world objects despite training solely in the simulation. The synthetic and real-world datasets will be published as well. © 2022, CC BY-NC-ND.

关键词： Physical properties

HIDDEN PARAMETER RECURRENT STATE SPACE MODELS FOR CHANGING DYNAMICS SCENARIOS 10

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HIDDEN PARAMETER RECURRENT STATE SPACE MODELS FOR CHANGING D...

10th International Conference on learning Representations, ICLR 2022

作者： Shaj, Vaisakh Büchler, Dieter Sonker, Rohit Becker, Philipp Neumann, Gerhard Autonomous Learning Robots KIT Germany LCAS University of Lincoln United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Indian Institute of Technology Kanpur India

Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification. However, these models assume that the dynamics are fixed and unchanging, which is rarely the case in real-world scenarios. Many control applications often exhibit tasks with similar but not identical dynamics which can be modeled as a latent variable. We introduce the Hidden Parameter Recurrent State Space Models (HiP-RSSMs), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors. We present a simple and effective way of learning and performing inference over this Gaussian graphical model that avoids approximations like variational inference. We show that HiP-RSSMs outperforms RSSMs and competing multi-task models on several challenging robotic benchmarks both on real-world systems and simulations. © 2022 ICLR 2022 - 10th International Conference on learning Representationss. All rights reserved.

关键词： Dynamics

End-to-End learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control

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arXiv 2022年

作者： Reuss, Moritz van Duijkeren, Niels Krug, Robert Becker, Philipp Shaj, Vaisakh Neumann, Gerhard Bosch Corporate Research Renningen Germany LCAS University Of Lincoln United Kingdom Intuitive Robots Lab Karlsruhe Institute of Technology Germany Autonomous Learning Robots Karlsruhe Institute of Technology Germany

It is well-known that inverse dynamics models can improve tracking performance in robot control. These models need to precisely capture the robot dynamics, which consist of well-understood components, e.g., rigid body dynamics, and effects that remain challenging to capture, e.g., stick-slip friction and mechanical flexibilities. Such effects exhibit hysteresis and partial observability, rendering them, particularly challenging to model. Hence, hybrid models, which combine a physical prior with data-driven approaches are especially well-suited in this setting. We present a novel hybrid model formulation that enables us to identify fully physically consistent inertial parameters of a rigid body dynamics model which is paired with a recurrent neural network architecture, allowing us to capture unmodeled partially observable effects using the network memory. We compare our approach against state-of-the-art inverse dynamics models on a 7 degree of freedom manipulator. Using data sets obtained through an optimal experiment design approach, we study the accuracy of offline torque prediction and generalization capabilities of joint learning methods. In control experiments on the real system, we evaluate the model as a feed-forward term for impedance control and show the feedback gains can be drastically reduced to achieve a given tracking accuracy. © 2022, CC BY-NC-ND.

关键词： Inverse problems

A learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand

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A Learning-based Controller for Multi-Contact Grasps on Unkn...

IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Dominik Winkelbauer Rudolph Triebel Berthold Bäuml DLR Institute of Robotics & Mechatronics Germany Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power grasps with multi-contacts, while operating self-contained on before unseen objects. No detailed contact information is needed, but only a rough 3D model, e.g., reconstructed from a single depth image. First, the external wrench being applied to the object is estimated by using the measured torques at the joints. Then, the torques necessary to counteract the estimated wrench while keeping the object at its initial pose are predicted. The torques are commanded via desired joint angles to an underlying joint-level impedance controller. To reach real-time performance, we propose a learning-based approach that is based on a wrench estimator- and a torque predictor neural network. Both networks are trained in a supervised fashion using data generated via the analytical formulation of the controller. In an extensive simulation-based evaluation, we show that our controller is able to keep 83.1% of the tested grasps stable when applying external wrenches with up to 10 N. At the same time, we outperform the two tested baselines by being more efficient and inducing less involuntary object movement. Finally, we show that the controller also works on the real DLR-Hand II, reaching a cycle time of 6 ms. Website: ***/grasping

关键词： Solid modeling Torque Three-dimensional displays Power measurement Neural networks Robot sensing systems Real-time systems Torque measurement Impedance Intelligent robots

Versatile Inverse Reinforcement learning via cumulative rewards

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arXiv 2021年

作者： Freymuth, Niklas Becker, Philipp Neumann, Gerhard Autonomous Learning Robots Karlsruhe Institute of Technology Karlsruhe Germany

Inverse Reinforcement learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert. Current approaches usually do this with generative and uni-modal models, meaning that they encode a single behavior. In the common setting, where there are various solutions to a problem and the experts show versatile behavior this severely limits the generalization capabilities of these methods. We propose a novel method for Inverse Reinforcement learning that overcomes these problems by formulating the recovered reward as a sum of iteratively trained discriminators. We show on simulated tasks that our approach is able to recover general, high-quality reward functions and produces policies of the same quality as behavioral cloning approaches designed for versatile behavior. © 2021, CC BY.

关键词： Encoding (symbols)

A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models

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arXiv 2022年

作者： Arenz, Oleg Dahlinger, Philipp Ye, Zihan Volpp, Michael Neumann, Gerhard Intelligent Autonomous Systems Technical University of Darmstadt Germany Autonomous Learning Robots Karlsruhe Institute of Technology Germany Technical University of Darmstadt Germany

Variational inference with Gaussian mixture models (GMMs) enables learning of highly tractable yet multi-modal approximations of intractable target distributions with up to a few hundred dimensions. The two currently most effective methods for GMM-based variational inference, VIPS and iBayes-GMM, both employ independent natural gradient updates for the individual components and their weights. We show for the first time, that their derived updates are equivalent, although their practical implementations and theoretical guarantees differ. We identify several design choices that distinguish both approaches, namely with respect to sample selection, natural gradient estimation, stepsize adaptation, and whether trust regions are enforced or the number of components adapted. We argue that for both approaches, the quality of the learned approximations can heavily suffer from the respective design choices: By updating the individual components using samples from the mixture model, iBayes-GMM often fails to produce meaningful updates to low-weight components, and by using a zero-order method for estimating the natural gradient, VIPS scales badly to higher-dimensional problems. Furthermore, we show that information-geometric trust-regions (used by VIPS) are effective even when using first-order natural gradient estimates, and often outperform the improved Bayesian learning rule (iBLR) update used by iBayesGMM. We systematically evaluate the effects of design choices and show that a hybrid approach significantly outperforms both prior works. Along with this work, we publish our highly modular and efficient implementation for natural gradient variational inference with Gaussian mixture models, which supports 432 different combinations of design choices, facilitates the reproduction of all our experiments, and may prove valuable for the practitioner. © 2022, CC BY-SA.

关键词： Gaussian distribution

A study on dense and sparse (Visual) rewards in robot policy learning

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arXiv 2021年

作者： Mohtasib, Abdalkarim Neumann, Gerhard Cuayáhuitl, Heriberto Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Autonomous Learning Robots Karlsruhe Institute of Technology Karlsruhe Germany

Deep Reinforcement learning (DRL) is a promising approach for teaching robots new behaviour. However, one of its main limitations is the need for carefully hand-coded reward signals by an expert. We argue that it is crucial to automate the reward learning process so that new skills can be taught to robots by their users. To address such automation, we consider task success classifiers using visual observations to estimate the rewards in terms of task success. In this work, we study the performance of multiple state-of-the-art deep reinforcement learning algorithms under different types of reward: Dense, Sparse, Visual Dense, and Visual Sparse rewards. Our experiments in various simulation tasks (Pendulum, Reacher, Pusher, and Fetch Reach) show that while DRL agents can learn successful behaviours using visual rewards when the goal targets are distinguishable, their performance may decrease if the task goal is not clearly visible. Our results also show that visual dense rewards are more successful than visual sparse rewards and that there is no single best algorithm for all tasks. © 2021, CC BY-NC-SA.

关键词： Deep learning

HIDDEN PARAMETER RECURRENT STATE SPACE MODELS FOR CHANGING DYNAMICS SCENARIOS

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arXiv 2022年

作者： Shaj, Vaisakh Büchler, Dieter Sonker, Rohit Becker, Philipp Neumann, Gerhard Autonomous Learning Robots KIT Germany LCAS University Of Lincoln United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Indian Institute Of Technology Kanpur India

关键词： Dynamics

Non-adversarial imitation learning and its connections to adversarial methods

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arXiv 2020年

作者： Arenz, Oleg Neumann, Gerhard Intelligent Autonomous Systems TU Darmstadt Germany Autonomous Learning Robots Karlsruhe Institute of Technology Germany Bosch Center for Artificial Intelligence Renningen Germany

Many modern methods for imitation learning and inverse reinforcement learning, such as GAIL or AIRL, are based on an adversarial formulation. These methods apply GANs to match the expert’s distribution over states and actions with the implicit state-action distribution induced by the agent’s policy. However, by framing imitation learning as a saddle point problem, adversarial methods can suffer from unstable optimization, and convergence can only be shown for small policy updates. We address these problems by proposing a framework for non-adversarial imitation learning. The resulting algorithms are similar to their adversarial counterparts and, thus, provide insights for adversarial imitation learning methods. Most notably, we show that AIRL is an instance of our non-adversarial formulation, which enables us to greatly simplify its derivations and obtain stronger convergence guarantees. We also show that our non-adversarial formulation can be used to derive novel algorithms by presenting a method for offline imitation learning that is inspired by the recent ValueDice algorithm, but does not rely on small policy updates for convergence. In our simulated robot experiments, our offline method for non-adversarial imitation learning seems to perform best when using many updates for policy and discriminator at each iteration and outperforms behavioral cloning and ValueDice. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods