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Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints

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arXiv 2017年

作者： Yildirim, Ilker Gerstenberg, Tobias Saeed, Basil Toussaint, Marc Tenenbaum, Joshua B. Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Machine Learning and Robotics Lab University of Stuttgart Germany

In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment 1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so. In Experiment 2, we asked participants online to judge whether they think the person in the lab used one or two hands. The results revealed a close correspondence between participants' actions in the lab, and the mental simulations of participants online. To explain participants' actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, executes the plan using a geometric solver, and checks the plan's feasibility by taking into account the physical constraints of the scene. Our model explains participants' actions and judgments to a high degree of quantitative accuracy. Copyright © 2017, The Authors. All rights reserved.

关键词： Geometry

Multi-bound tree search for logic-geometric programming in cooperative manipulation domains

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Multi-bound tree search for logic-geometric programming in c...

IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Toussaint Manuel Lopes Machine Learning and Robotics Lab University of Stuttgart Germany INESC-ID Instituto Superior Técnico Universide de Lisboa Portugal

Joint symbolic and geometric planning is one of the core challenges in robotics. We address the problem of multi-agent cooperative manipulation, where we aim for jointly optimal paths for all agents and over the full manipulation sequence. This joint optimization problem can be framed as a logic-geometric program. Existing solvers lack several features (such as consistently handling kinematic switches) and efficiency to handle the cooperative manipulation domain. We propose a new approximate solver scheme, combining ideas from branch-and-bound and MCTS and exploiting multiple levels of bounds to better direct the search. We demonstrate the method in a scenario where a Baxter robot needs to help a human to reach for objects.

关键词： Kinematics Optimization Planning Aerospace electronics Programming Robots Search problems

An Interior Point Method Solving Motion Planning Problems with Narrow Passages

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arXiv 2020年

作者： Mainprice, Jim Ratliff, Nathan Toussaint, Marc Schaal, Stefan Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPITübingen & Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany

Algorithmic solutions for the motion planning problem have been investigated for five decades. Since the development of A* in 1969 many approaches have been investigated, traditionally classified as either grid decomposition, potential fields or sampling-based. In this work, we focus on using numerical optimization, which is understudied for solving motion planning problems. This lack of interest in the favor of sampling-based methods is largely due to the non-convexity introduced by narrow passages. We address this shortcoming by grounding the solution in differential geometry. We demonstrate through a series of experiments on 3 Dofs and 6 Dofs narrow passage problems, how modeling explicitly the underlying Riemannian manifold leads to an efficient interior point non-linear programming solution. 1. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear programming

Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning

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arXiv 2020年

作者： Toussaint, Marc Ha, Jung-Su Driess, Danny Machine Learning & Robotics Lab University Stuttgart Stuttgart70569 Germany Max Planck Institute for Intelligent Systems Stuttgart70569 Germany

Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose path descriptions of physics which directly allow us to leverage optimization methods to solve planning problems, using multi-physics descriptions that enable the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target. Copyright © 2020, The Authors. All rights reserved.

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Deep 6-DoF tracking of unknown objects for reactive grasping

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arXiv 2021年

作者： Tuscher, Marc Hörz, Julian Driess, Danny Toussaint, Marc Sereact Germany Machine Learning and Robotics Lab University of Stuttgart Germany Max-Planck Institute for Intelligent Systems Stuttgart Germany Learning and Intelligent Systems TU Berlin Germany

Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning. Our object tracking method combines Siamese Networks with an Iterative Closest Point approach for pointcloud registration into a method for 6-DoF unknown object tracking. The method does not require further training and is robust to noise and occlusion. We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points. © 2021, CC BY.

关键词： Iterative methods

Control-Barrier-Aided Teleoperation with Visual-Inertial SLAM for Safe MAV Navigation in Complex Environments

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Control-Barrier-Aided Teleoperation with Visual-Inertial SLA...

IEEE International Conference on robotics and Automation (ICRA)

作者： Siqi Zhou Sotiris Papatheodorou Stefan Leutenegger Angela P. Schoellig Learning Systems and Robotics Lab School of Computation Information and Technology Technical University of Munich Munich Institute of Robotics and Machine Intelligence (MIRMI) Smart Robotics Lab School of Computation Information and Technology Technical University of Munich Department of Computing Smart Robotics Lab Imperial College London

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we consider a Micro Aerial Vehicle (MAV) system teleoperated by a non-expert and introduce a perceptive safety filter that leverages Control Barrier Functions (CBFs) in conjunction with Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) and dense 3D occupancy mapping to guarantee safe navigation in complex and unstructured environments. Our system relies solely on onboard IMU measurements, stereo infrared images, and depth images and autonomously corrects teleoperated inputs when they are deemed unsafe. We define a point in 3D space as unsafe if it satisfies either of two conditions: (i) it is occupied by an obstacle, or (ii) it remains unmapped. At each time step, an occupancy map of the environment is updated by the VI-SLAM by fusing the onboard measurements, and a CBF is constructed to parameterize the (un)safe region in the 3D space. Given the CBF and state feedback from the VI-SLAM module, a safety filter computes a certified reference that best matches the teleoperation input while satisfying the safety constraint encoded by the CBF. In contrast to existing perception-based safe control frameworks, we directly close the perception-action loop and demonstrate the full capability of safe control in combination with real-time VI-SLAM without any external infrastructure or prior knowledge of the environment. We verify the efficacy of the perceptive safety filter in real-time MAV experiments using exclusively onboard sensing and computation and show that the teleoperated MAV is able to safely navigate through unknown environments despite arbitrary inputs sent by the teleoperator.

关键词： Teleoperators State feedback Three-dimensional displays Simultaneous localization and mapping Navigation Real-time systems Time measurement

An Interior Point Method Solving Motion Planning Problems with Narrow Passages

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An Interior Point Method Solving Motion Planning Problems wi...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jim Mainprice Nathan Ratliff Marc Toussaint Stefan Schaal Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen & Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

关键词：

learning to arbitrate human and robot control using disagreement between sub-policies

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arXiv 2021年

作者： Oh, Yoojin 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 MPI-IS Tübingen/Stuttgart Germany

In the context of teleoperation, arbitration refers to deciding how to blend between human and autonomous robot commands. We present a reinforcement learning solution that learns an optimal arbitration strategy that allocates more control authority to the human when the robot comes across a decision point in the task. A decision point is where the robot encounters multiple options (sub-policies), such as having multiple paths to get around an obstacle or deciding between two candidate goals. By expressing each directional sub-policy as a von Mises distribution, we identify the decision points by observing the modality of the mixture distribution. Our reward function reasons on this modality and prioritizes to match its learned policy to either the user or the robot accordingly. We report teleoperation experiments on reach-and-grasping objects using a robot manipulator arm with different simulated human controllers. Results indicate that our shared control agent outperforms direct control and improves the teleoperation performance among different users. Using our reward term enables flexible blending between human and robot commands while maintaining safe and accurate teleoperation. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote control

Latent Action Priors for Locomotion with Deep Reinforcement learning

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arXiv 2024年

作者： Hausdörfer, Oliver von Rohr, Alexander Lefort, Éric Schoellig, Angela P. The Technical University of Munich Germany TUM School of Computation Information and Technology Department of Computer Engineering Learning Systems and Robotics Lab Germany Munich Institute of Robotics and Machine Intelligence Germany

Deep Reinforcement learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algorithms, the resulting solutions are often brittle and appear unnatural. This is especially true for learning direct joint-level torque control, as inductive biases are difficult to integrate into the learning process. We propose an inductive bias for learning locomotion that is especially useful for torque control: latent actions learned from a small dataset of expert demonstrations. This prior allows the policy to directly leverage knowledge contained in the expert’s actions and facilitates more efficient exploration. We observe that the agent is not restricted to the reward levels of the demonstration, and performance in transfer tasks is improved significantly. Latent action priors combined with style rewards for imitation leads to a closer replication of the expert’s behavior. Videos and code are available at https://***/view/ latent-action-priors. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning