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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

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

Robust task and motion planning for long-horizon architectural construction planning

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arXiv 2020年

作者： Hartmann, Valentin N. Oguz, Ozgur S. Driess, Danny Toussaint, Marc Menges, Achim Machine Learning & Robotics Lab. University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Institute for Computational Design and Construction University of Stuttgart Germany

Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems enables design analysis tools and facilitates faster design iteration cycles for designers and engineers. However, generic task-and-motion planning (TAMP) for long-horizon construction processes is beyond the capabilities of current approaches. In this paper, we develop a multi-agent TAMP framework for long horizon problems such as constructing a full-scale building. To this end we extend the Logic-Geometric Programming framework by sampling-based motion planning, a limited horizon approach, and a task-specific structural stability optimization that allow an effective decomposition of the task. We show that our framework is capable of constructing a large pavilion built from several hundred geometrically unique building elements from start to end autonomously. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

Deep workpiece region segmentation for bin picking

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arXiv 2019年

作者： Khalid, Muhammad Usman Hager, Janik M. Kraus, Werner Huber, Marco F. Toussaint, Marc Robot and Assistive Systems Fraunhofer IPA Stuttgart Germany Machine Learning & Robotics Lab University of Stuttgart Germany Fraunhofer IPA Stuttgart and Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

For most industrial bin picking solutions, the pose of a workpiece is localized by matching a CAD model to point cloud obtained from 3D sensor. Distinguishing flat workpieces from bottom of the bin in point cloud imposes challenges in the localization of workpieces that lead to wrong or phantom detections. In this paper, we propose a framework that solves this problem by automatically segmenting workpiece regions from non-workpiece regions in a point cloud data. It is done in real time by applying a fully convolutional neural network trained on both simulated and real data. The real data has been labelled by our novel technique which automatically generates ground truth labels for real point clouds. Along with real time workpiece segmentation, our framework also helps in improving the number of detected workpieces and estimating the correct object poses. Moreover, it decreases the computation time by approximately 1s due to a reduction of the search space for the object pose estimation. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical detection

learning efficient constraint graph sampling for robotic sequential manipulation

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arXiv 2020年

作者： Ortiz-Haro, Joaquim Hartmann, Valentin N. Oguz, Ozgur S. Toussaint, Marc Machine Learning & Robotics Lab. University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Learning and Intelligent Systems Lab. TU Berlin Germany

Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the underlying nonlinear program is decomposed into differentiable equality and inequality constraints, each of which depends only on some variables. Such problems are at the core of efficient and robust sequential robot manipulation planning. Naive sequential conditional sampling of individual variables, as well as fully joint sampling of all variables at once (e.g., leveraging optimization methods), can be highly inefficient and non-robust. We propose a novel framework to learn how to break the overall problem into smaller sequential sampling problems. Specifically, we leverage Monte-Carlo Tree Search to learn assignment orders for the variable-subsets, in order to minimize the computation time to generate feasible full samples. This strategy allows us to efficiently compute a set of diverse valid robot configurations for mode-switches within sequential manipulation tasks, which are waypoints for subsequent trajectory optimization or sampling-based motion planning algorithms. We show that the learning method quickly converges to the best sampling strategy for a given problem, and outperforms user-defined orderings or fully joint optimization, while providing a higher sample diversity. © 2020, CC BY.

关键词： Constraint theory

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

Temporal segmentation of pair-wise interaction phases in sequential manipulation demonstrations

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Temporal segmentation of pair-wise interaction phases in seq...

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： A. Baisero Y. Mollard M. Lopes M. Toussaint I. Lutkebohle Machine Learning and Robotics Lab University of Stuttgart Germany Flowers Team French Institute for Research in Computer Science and Automation (Inria) France

We consider the problem of learning from complex sequential demonstrations. We propose to analyze demonstrations in terms of the concurrent interaction phases which arise between pairs of involved bodies (hand-object and object-object). These interaction phases are the key to decompose a full demonstration into its atomic manipulation actions and to extract their respective consequences. In particular, one may assume that the goal of each interaction phase is to achieve specific geometric constraints between objects. This generalizes previous learning from Demonstration approaches by considering not just the motion of the end-effector but also the relational properties of the objects' motion. We present a linear-chain Conditional Random Field model to detect the pair-wise interaction phases and extract the geometric constraints that are established in the environment, which represent a high-level task oriented description of the demonstrated manipulation. We test our system on single- and multi-agent demonstrations of assembly tasks, respectively of a wooden toolbox and a plastic chair.

关键词： Motion segmentation Trajectory Hidden Markov models Assembly Quaternions Service robots

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

Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations

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arXiv 2024年

作者： Hagnberger, Jan Kalimuthu, Marimuthu Musekamp, Daniel Niepert, Mathias Machine Learning and Simulation Lab Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany Germany

Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of Transformers, such as quadratic memory and time complexity. Furthermore, all prevalent architectures for PDE solving lack at least one of several desirable properties of an ideal surrogate model, such as (i) generalization to PDE parameters not seen during training, (ii) spatial and temporal zero-shot super-resolution, (iii) continuous temporal extrapolation, (iv) support for 1D, 2D, and 3D PDEs, and (v) efficient inference for longer temporal rollouts. To address these limitations, we propose Vectorized Conditional Neural Fields (VCNeFs), which represent the solution of time-dependent PDEs as neural fields. Contrary to prior methods, however, VCNeFs compute, for a set of multiple spatio-temporal query points, their solutions in parallel and model their dependencies through attention mechanisms. Moreover, VCNeF can condition the neural field on both the initial conditions and the parameters of the PDEs. An extensive set of experiments demonstrates that VCNeFs are competitive with and often outperform existing ML-based surrogate models. Copyright © 2024, The Authors. All rights reserved.

关键词： Extrapolation