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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

PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation

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PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Esti...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Abdallah Benzine Florian Chabot Bertrand Luvison Quoc Cuong Pham Catherine Achard CEA LIST Vision and Learning Lab for Scene Analysis Sorbonne University CNRS Institute for Intelligent Systems and Robotics

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recently, several deep learning models have been proposed for 3D human pose estimation. Nevertheless, most of these approaches only focus on the single-person case or estimate 3D pose of a few people at high resolution. Furthermore, many applications such as autonomous driving or crowd analysis require pose estimation of a large number of people possibly at low-resolution. In this work, we present PandaNet (Pose estimAtioN and Dectection Anchor-based Network), a new single-shot, anchor-based and multi-person 3D pose estimation approach. The proposed model performs bounding box detection and, for each detected person, 2D and 3D pose regression into a single forward pass. It does not need any post-processing to regroup joints since the network predicts a full 3D pose for each bounding box and allows the pose estimation of a possibly large number of people at low resolution. To manage people overlapping, we introduce a Pose-Aware Anchor Selection strategy. Moreover, as imbalance exists between different people sizes in the image, and joints coordinates have different uncertainties depending on these sizes, we propose a method to automatically optimize weights associated to different people scales and joints for efficient training. PandaNet surpasses previous single-shot methods on several challenging datasets: a multi-person urban virtual but very realistic dataset (JTA Dataset), and two real world 3D multi-person datasets (CMU Panoptic and MuPoTS-3D).

关键词： Three-dimensional displays Two dimensional displays Pose estimation Heating systems Image resolution Solid modeling Skeleton

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

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

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：

Deep Visual Heuristics: learning Feasibility of Mixed-Integer Programs for Manipulation Planning

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Deep Visual Heuristics: Learning Feasibility of Mixed-Intege...

IEEE International Conference on robotics and Automation (ICRA)

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

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper, we propose a deep neural network that predicts the feasibility of a mixed-integer program from visual input for robot manipulation planning. Integrating learning into task and motion planning is challenging, since it is unclear how the scene and goals can be encoded as input to the learning algorithm in a way that enables to generalize over a variety of tasks in environments with changing numbers of objects and goals. To achieve this, we propose to encode the scene and the target object directly in the image *** experiments show that our proposed network generalizes to scenes with multiple objects, although during training only two objects are present at the same time. By using the learned network as a heuristic to guide the search over the discrete variables of the mixed-integer program, the number of optimization problems that have to be solved to find a feasible solution or to detect infeasibility can greatly be reduced.

关键词： Planning Task analysis Robot sensing systems Neural networks Grasping Search problems

Planning Coordinated Human-Robot Motions with Neural Network Full-Body Prediction Models

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arXiv 2022年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim The Machine Learning and Robotics Lab University of Stuttgart Germany The Humans to Robots Motions Research Group University of Stuttgart Germany The Learning and Intelligent Systems Lab TU Berlin Germany

Numerical optimization has become a popular approach to plan smooth motion trajectories for robots. However, when sharing space with humans, balancing properly safety, comfort and efficiency still remains challenging. This is notably the case because humans adapt their behavior to that of the robot, raising the need for intricate planning and prediction. In this paper, we propose a novel optimization-based motion planning algorithm, which generates robot motions, while simultaneously maximizing the human trajectory likelihood under a data-driven predictive model. Considering planning and prediction together allows us to formulate objective and constraint functions in the joint human-robot state space. Key to the approach are added latent space modifiers to a differentiable human predictive model based on a dedicated recurrent neural network. These modifiers allow to change the human prediction within motion optimization. We empirically evaluate our method using the publicly available MoGaze [1] dataset. Our results indicate that the proposed framework outperforms current baselines for planning handover trajectories and avoiding collisions between a robot and a human. Our experiments demonstrate collaborative motion trajectories, where both, the human prediction and the robot plan, adapt to each other. © 2022, CC BY.

关键词： Forecasting

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

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arXiv 2024年

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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

Distilling motion planner augmented policies into visual control policies for robot manipulation

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arXiv 2021年

作者： Liu, I-Chun Arthur Uppal, Shagun Sukhatme, Gaurav S. Lim, Joseph J. Englert, Peter Lee, Youngwoon Cognitive Learning for Vision and Robotics Lab University of Southern California Robotic Embedded Systems Laboratory University of Southern California

learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations. Prior work tackles the exploration problem by integrating motion planning and reinforcement learning. However, the motion planner augmented policy requires access to state information, which is often not available in the real-world settings. To this end, we propose to distill a state-based motion planner augmented policy to a visual control policy via (1) visual behavioral cloning to remove the motion planner dependency along with its jittery motion, and (2) vision-based reinforcement learning with the guidance of the smoothed trajectories from the behavioral cloning agent. We evaluate our method on three manipulation tasks in obstructed environments and compare it against various reinforcement learning and imitation learning baselines. The results demonstrate that our framework is highly sample-efficient and outperforms the state-of-the-art algorithms. Moreover, coupled with domain randomization, our policy is capable of zero-shot transfer to unseen environment settings with distractors. Code and videos are available at https://***/mopa-pd. © 2021, CC BY.

关键词： Motion planning