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Prediction of human full-body movements with motion optimization and recurrent neural networks

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arXiv 2019年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen Germany

Human movement prediction is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose a prediction framework that decouples short-Term prediction, linked to internal body dynamics, and long-Term prediction, linked to the environment and task constraints. In this work we investigate encoding short-Term dynamics in a recurrent neural network, while we account for environmental constraints, such as obstacle avoidance, using gradient-based trajectory optimization. Experiments on real motion data demonstrate that our framework improves the prediction with respect to state-of-The-Art motion prediction methods, as it accounts to beforehand unseen environmental structures. Moreover we demonstrate on an example, how this framework can be used to plan robot trajectories that are optimized to coordinate with a human partner. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion estimation

Deep visual reasoning: learning to predict action sequences for task and motion planning from an initial scene image

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arXiv 2020年

作者： Driess, Danny Ha, Jung-Su Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart Germany Max-Planck Institute for Intelligent Systems Stuttgart Germany Learning and Intelligent Systems Group TU Berlin Germany

In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g. first-order logic) with continuous motion planning such as nonlinear trajectory optimization. Due to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we develop a neural network which, based on an initial image of the scene, directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. A key aspect is that our method generalizes to scenes with many and varying number of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector. Results show runtime improvements of several magnitudes. Video: https://***/i8yyEbbvoEk Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

Plan-based relaxed reward shaping for goal-directed tasks

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arXiv 2021年

作者： Schubert, Ingmar Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Machine Learning and Robotics Lab University of Stuttgart Germany

In high-dimensional state spaces, the usefulness of Reinforcement learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict optimality guarantees of PB-RS to a guarantee of preserved long-term behavior. Being less restrictive, FV-RS allows for reward shaping functions that are even better suited for improving the sample efficiency of RL algorithms. In particular, we consider settings in which the agent has access to an approximate plan. Here, we use examples of simulated robotic manipulation tasks to demonstrate that plan-based FV-RS can indeed significantly improve the sample efficiency of RL over plan-based PB-RS. © 2021, CC BY.

关键词： Efficiency

Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters

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Practical Considerations for Discrete-Time Implementations o...

American Control Conference (ACC)

作者： Lukas Brunke Siqi Zhou Mingxuan Che Angela P. Schoellig Learning Systems and Robotics Lab Technical University of Munich Germany University of Toronto Canada Munich Institute of Robotics and Machine Intelligence (MIRMI) the University of Toronto Robotics Institute and the Vector Institute for Artificial Intelligence

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement learning. Here, safety is defined as staying in a pre-defined set, the safe set, that adheres to the system's state constraints, e.g., as given by lane boundaries for a self-driving vehicle. In this paper, we examine one commonly overlooked problem that arises in practical implementations of continuous-time CBF-based safety filters. In particular, we look at the issues caused by discrete-time implementations of the continuous-time CBF-based safety filter, especially for cases where the magnitude of the Lie derivative of the CBF with respect to the control input is zero or close to zero. When overlooked, this filter can result in undesirable chattering effects or constraint violations. In this work, we propose three mitigation strategies that allow us to use a continuous-time safety filter in a discrete-time implementation with a local relative degree. Using these strategies in augmented CBF-based safety filters, we achieve safety for all states in the safe set by either using an additional penalty term in the safety filtering objective or modifying the CBF such that those undesired states are not encountered during closed-loop operation. We demonstrate the presented issue and validate our three proposed mitigation strategies in simulation and on a real-world quadrotor.

关键词： Filters Prevention and mitigation Transforms Switches Reinforcement learning Control systems Safety

The Markov decision process extraction network

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The Markov decision process extraction network

18th European Symposium on Artificial Neural Networks, Computational Intelligence and machine learning, ESANN 2010

作者： Duell, Siegmund Hans, Alexander Udluft, Steffen Siemens AG Corporate Research and Technologies Learning Systems Otto-Hahn-Ring 6 D-81739 Munich Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 D-10587 Berlin Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 D-98684 Ilmenau Germany

ISBN: (纸本)2930307102

This paper presents the Markov decision process extraction network, which is a data-efficient, automatic state estimation approach for discrete-time reinforcement learning (RL) based on recurrent neural networks. The architecture is designed to model the minimal relevant dynamics of an environment, capable of condensing large sets of continuous observables to a compact state representation and excluding irrelevant information. To the best of our knowledge, it is the first approach published to automatically extract minimal relevant aspects of the dynamics from observations to model a Markov decision process, suitable for RL, without requiring special knowledge of the regarded environment. The capabilities of the neural state estimation approach are evaluated using the cart-pole problem and standard table-based policy iteration.

关键词： Markov processes

Anticipating Human Intention for Full-Body Motion Prediction in Object Grasping and Placing Tasks

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Anticipating Human Intention for Full-Body Motion Prediction...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Philipp Kratzer Niteesh Balachandra Midlagajni Marc Toussaint Jim Mainprice Machine Learning and Robotics Lab University of Stuttgart Germany Humans to Robots Motions Research Group HRM University of Stuttgart Germany Learning and Intelligent Systems Lab Technical University of Berlin Germany

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Motion prediction in unstructured environments is a difficult problem and is essential for safe and efficient human-robot space sharing and collaboration. In this work, we focus on manipulation movements in environments such as homes, workplaces or restaurants, where the overall task and environment can be leveraged to produce accurate motion prediction. For these cases we propose an algorithmic framework that accounts explicitly for the environment geometry based on a model of affordances and a model of short-term human dynamics both trained on motion capture data. We propose dedicated function networks for graspability and placebility affordances and we make use of a dedicated RNN [1] for short-term motion prediction. The prediction of grasp and placement probability densities are used by a constraint-based trajectory optimizer to produce a full-body motion prediction over the entire horizon. We show by comparing to ground truth data that we achieve similar performance for full-body motion predictions as using oracle grasp and place locations.

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Natural Gradient Shared Control

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Natural Gradient Shared Control

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yoojin Oh Shao-Wen Wu Marc Toussaint Jim Mainprice Machine Learning and Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen Germany

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

We propose a formalism for shared control, which is the problem of defining a policy that blends user control and autonomous control. The challenge posed by the shared autonomy system is to maintain user control authority while allowing the robot to support the user. This can be done by enforcing constraints or acting optimally when the intent is clear. Our proposed solution relies on natural gradients emerging from the divergence constraint between the robot and the shared policy. We approximate the Fisher information by sampling a learned robot policy and computing the local gradient to augment the user control when necessary. A user study performed on a manipulation task demonstrates that our approach allows for more efficient task completion while keeping control authority against a number of baseline methods.

关键词：

Anticipating Human Intention for Full-Body Motion Prediction in Object Grasping and Placing Tasks

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arXiv 2020年

作者： Kratzer, Philipp Midlagajni, Niteesh Balachandra Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Humans to Robots Motions Research Group HRM University of Stuttgart Germany Learning and Intelligent Systems Lab Technical University of Berlin Germany

关键词： Motion estimation

Natural gradient shared control

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arXiv 2020年

作者： Oh, Yoojin Wu, Shao-Wen Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen Germany

关键词： Fisher information matrix