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A Survey of Optimization-Based Task and Motion Planning: From Classical to learning Approaches

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年

作者： Zhao, Zhigen Cheng, Shuo Ding, Yan Zhou, Ziyi Zhang, Shiqi Xu, Danfei Zhao, Ye Georgia Inst Technol he Lab Intelligent Decis & Autonomous Robots LIDAR Atlanta GA 30318 USA Georgia Inst Technol Robot Learning & Reasoning Lab RL2 Atlanta GA 30318 USA Binghamton Univ Autonomous Intelligent Robot AIR Grp Binghamton NY 13902 USA

Task and motion planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic tasks. Optimization-based TAMP focuses on hybrid optimization approaches that define goal conditions via objective functions and are capable of handling open-ended goals, robotic dynamics, and physical interaction between the robot and the environment. Therefore, optimization-based TAMP is particularly suited to solve highly complex, contact-rich locomotion and manipulation problems. This survey provides a comprehensive review on optimization-based TAMP, covering first, planning domain representations, including action description languages and temporal logic, second, individual solution strategies for components of TAMP, including AI planning and trajectory optimization (TO), and finally, the dynamic interplay between logic-based task planning and model-based TO. A particular focus of this survey is to highlight the algorithm structures to efficiently solve TAMP, especially hierarchical and distributed approaches. In addition, the survey emphasizes the synergy between the classical methods and contemporary learning-based innovations, such as large language models. Furthermore, the future research directions for TAMP is discussed in this survey, highlighting both algorithmic and application-specific challenges.

关键词： AI planning large language models (LLMs) robot learning task and motion planning (TAMP) temporal logic trajectory optimization (TO)

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Swarm Reinforcement learning for Adaptive Mesh Refinement

arXiv

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arXiv 2023年

作者： Freymuth, Niklas Dahlinger, Philipp Würth, Tobias Reisch, Simon Kärger, Luise Neumann, Gerhard Autonomous Learning Robots Karlsruhe Institute of Technology Karlsruhe Germany Institute of Vehicle Systems Technology Karlsruhe Institute of Technology Karlsruhe Germany

Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on heuristics or expensive error estimators, hindering their use for complex simulations. Recent learning-based AMR methods tackle these issues, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate our approach, Adaptive Swarm Mesh Refinement (ASMR), on challenging refinement tasks. Our approach learns reliable and efficient refinement strategies that can robustly generalize to different domains during inference. Additionally, it achieves a speedup of up to 2 orders of magnitude compared to uniform refinements in more demanding simulations. We outperform learned baselines and heuristics, achieving a refinement quality that is on par with costly error-based oracle AMR strategies. © 2023, CC BY.

关键词： Markov processes

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Robot Policy learning from Demonstration Using Advantage Weighting and Early Termination

arXiv

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arXiv 2022年

作者： Mohtasib, Abdalkarim Neumann, Gerhard Cuayáhuitl, Heriberto University of Lincoln Lincoln United Kingdom Autonomous Learning Robots KIT Karlsruhe Germany

learning robotic tasks in the real world is still highly challenging and effective practical solutions remain to be found. Traditional methods used in this area are imitation learning and reinforcement learning, but they both have limitations when applied to real robots. Combining reinforcement learning with pre-collected demonstrations is a promising approach that can help in learning control policies to solve robotic tasks. In this paper, we propose an algorithm that uses novel techniques to leverage offline expert data using offline and online training to obtain faster convergence and improved performance. The proposed algorithm (AWET) weights the critic losses with a novel agent advantage weight to improve over the expert data. In addition, AWET makes use of an automatic early termination technique to stop and discard policy rollouts that are not similar to expert trajectories-to prevent drifting far from the expert data. In an ablation study, AWET showed improved and promising performance when compared to state-of-the-art baselines on four standard robotic tasks. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

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Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations

arXiv

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arXiv 2024年

作者： Freymuth, Niklas Dahlinger, Philipp Würth, Tobias Becker, Philipp Taranovic, Aleksandar Grönheim, Onno Kärger, Luise Neumann, Gerhard Autonomous Learning Robots Karlsruhe Institute of Technology Karlsruhe Germany Institute of Vehicle Systems Technology Karlsruhe Institute of Technology Karlsruhe Germany EVAGO GmbH Leonberg Germany

Many engineering systems require accurate simulations of complex physical systems. Yet, analytical solutions are only available for simple problems, necessitating numerical approximations such as the Finite Element Method (FEM). The cost and accuracy of the FEM scale with the resolution of the underlying computational mesh. To balance computational speed and accuracy meshes with adaptive resolution are used, allocating more resources to critical parts of the geometry. Currently, practitioners often resort to hand-crafted meshes, which require extensive expert knowledge and are thus costly to obtain. Our approach, Adaptive Meshing By Expert Reconstruction (AMBER), views mesh generation as an imitation learning problem. AMBER combines a graph neural network with an online data acquisition scheme to predict the projected sizing field of an expert mesh on a given intermediate mesh, creating a more accurate subsequent mesh. This iterative process ensures efficient and accurate imitation of expert mesh resolutions on arbitrary new geometries during inference. We experimentally validate AMBER on heuristic 2D meshes and 3D meshes provided by a human expert, closely matching the provided demonstrations and outperforming a single-step CNN baseline. © 2024, CC BY.

关键词： Mesh generation

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Specializing Versatile Skill Libraries using Local Mixture of Experts

arXiv

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arXiv 2021年

作者： Celik, Onur Zhou, Dongzhuoran Li, Ge Becker, Philipp Neumann, Gerhard Autonomous Learning Robots KIT Germany

A long-cherished vision in robotics is to equip robots with skills that match the versatility and precision of humans. For example, when playing table tennis, a robot should be capable of returning the ball in various ways while precisely placing it at the desired location. A common approach to model such versatile behavior is to use a Mixture of Experts (MoE) model, where each expert is a contextual motion primitive. However, learning such MoEs is challenging as most objectives force the model to cover the entire context space, which prevents specialization of the primitives resulting in rather low-quality components. Starting from maximum entropy reinforcement learning (RL), we decompose the objective into optimizing an individual lower bound per mixture component. Further, we introduce a curriculum by allowing the components to focus on a local context region, enabling the model to learn highly accurate skill representations. To this end, we use local context distributions that are adapted jointly with the expert primitives. Our lower bound advocates an iterative addition of new components, where new components will concentrate on local context regions not covered by the current MoE. This local and incremental learning results in a modular MoE model of high accuracy and versatility, where both properties can be scaled by adding more components on the fly. We demonstrate this by an extensive ablation and on two challenging simulated robot skill learning tasks. We compare our achieved performance to LaDiPS and HiREPS, a known hierarchical policy search method for learning diverse skills. © 2021, CC BY.

关键词： Curricula

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Deep Black-Box Reinforcement learning with Movement Primitives

arXiv

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arXiv 2022年

作者： Otto, Fabian Celik, Onur Zhou, Hongyi Ziesche, Hanna Vien, Ngo Anh Neumann, Gerhard Bosch Center for AI Germany University of Tübingen Germany Autonomous Learning Robots KIT Germany

Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a controller, often represented as a movement primitive, for a given task descriptor called a context. ERL offers several distinct benefits in comparison to step-based RL. It generates smooth control trajectories, can handle non-Markovian reward definitions, and the resulting exploration in parameter space is well suited for solving sparse reward settings. Yet, the high dimensionality of the movement primitive parameters has so far hampered the effective use of deep RL methods. In this paper, we present a new algorithm for deep ERL. It is based on differentiable trust region layers, a successful on-policy deep RL algorithm. These layers allow us to specify trust regions for the policy update that are solved exactly for each state using convex optimization, which enables policies learning with the high precision required for the ERL. We compare our ERL algorithm to state-of-the-art step-based algorithms in many complex simulated robotic control tasks. In doing so, we investigate different reward formulations - dense, sparse, and non-Markovian. While step-based algorithms perform well only on dense rewards, ERL performs favorably on sparse and non-Markovian rewards. Moreover, our results show that the sparse and the non-Markovian rewards are also often better suited to define the desired behavior, allowing us to obtain considerably higher quality policies compared to step-based RL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Intent-Aware Predictive Haptic Guidance and its Application to Shared Control Teleoperation 30

Intent-Aware Predictive Haptic Guidance and its Application ...

引用

30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

作者： Ly, Kim Tien Poozhiyil, Mithun Pandya, Harit Neumann, Gerhard Kucukyilmaz, Ayse Univ Nottingham Sch Comp Sci Nottingham England Toshiba Res Europe Cambridge England KIT Autonomous Learning Robots Karlsruhe Germany Univ Lincoln Lincoln Ctr Autonomous Syst L CAS Lincoln England

ISBN: (纸本)9781665404921

This paper presents a haptic shared control paradigm that modulates the level of robotic guidance, based on predictions of human motion intentions. The proposed method incorporates robot trajectories learned from human demonstrations and dynamically adjusts the level of robotic assistance based on how closely the detected intentions match these trajectories. An experimental study is conducted to demonstrate the paradigm on a teleoperated pick-and-place task using a Franka Emika Panda robot arm, controlled via a 3D Systems Touch X haptic interface. In the experiment, the human operator teleoperates a remote robot arm while observing the environment on a 2D screen. While the human teleoperates the robot arm, the objects are tracked, and the human's motion intentions (e.g., which object will be picked or which bin will be approached) are predicted using a Deep Q-Network (DQN). The predictions are made considering the current robot state and baseline robot trajectories that are learned from human demonstrations using Probabilistic Movement Primitives (ProMPs). The detected intentions are then used to condition the ProMP trajectories to modulate the movement and accommodate changing object configurations. Consequently, the system generates adaptive force guidance as weighted virtual fixtures that are rendered on the haptic device. The outcomes of the user study, conducted with 12 participants, indicate that the proposed paradigm can successfully guide users to robust grasping configurations and brings better performance by reducing the number of grasp attempts and improving trajectory smoothness and length.

关键词： Performance evaluation Adaptive systems Three-dimensional displays Tracking Navigation Grasping Manipulators

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

arXiv

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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

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MP3: Movement Primitive-Based (Re-)Planning Policy

arXiv

引用

arXiv 2023年

作者： Otto, Fabian Zhou, Hongyi Celik, Onur Li, Ge Lioutikov, Rudolf Neumann, Gerhard Bosch Center for Artificial Intelligence University of Tübingen Tübingen72076 Germany Intuitive Robots Lab Karlsruhe Institute of Technology Karlsruhe76131 Germany Autonomous Learning Robots Lab Karlsruhe Institute of Technology Karlsruhe76131 Germany

We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL framework, MP3 enables the generation of smooth trajectories throughout the whole learning process while effectively learning from sparse and non-Markovian rewards. Additionally, MP3 maintains the capability to adapt to changes in the environment during execution. Although many early successes in robot RL have been achieved by combining RL with MPs, these approaches are often limited to learning single stroke-based motions, lacking the ability to adapt to task variations or adjust motions during execution. Building upon our previous work, which introduced an episode-based RL method for the non-linear adaptation of MP parameters to different task variations, this paper extends the approach to incorporating replanning strategies. This allows adaptation of the MP parameters throughout motion execution, addressing the lack of online motion adaptation in stochastic domains requiring feedback. We compared our approach against state-of-the-art deep RL and RL with MPs methods. The results demonstrated improved performance in sophisticated, sparse reward settings and in domains requiring replanning. The video demonstration can be accessed at https://***/mp3_website/. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Meta-learning Regrasping Strategies for Physical-Agnostic Objects

arXiv

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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

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