检索结果-内蒙古大学图书馆

An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Henkel, Christian Abbenseth, Jannik Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany

— We consider multi-agent transport task problems where, e.g. in a factory setting, items have to be delivered from a given start to a goal pose while the delivering robots need to avoid collisions with each other on the floor. We introduce a Task Conflict-Based Search (TCBS) Algorithm to solve the combined delivery task allocation and multi-agent path planning problem optimally. The problem is known to be NP-hard and the optimal solver cannot scale. However, we introduce it as a baseline to evaluate the sub-optimality of other approaches. We show experimental results that compare our solver with different sub-optimal ones in terms of regret. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

learning efficient constraint graph sampling for robotic sequential manipulation

学校读者我要写书评

暂无评论

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

An efficient minimum vocabulary construction algorithm for language modeling 12

学校读者我要写书评

暂无评论

25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012

作者： Lin, Sina Qin, Zengchang Huang, Zehua Wan, Tao Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing China School of Advanced Engineering Beihang University China Robotics Institute Carnegie Mellon University Pittsburgh United States School of Medicine Boston University Boston United States

ISBN: (纸本)9783642310867

In learning a new word by a dictionary, we first need to know a set of "basic words" which are frequently appeared in word definitions. It often happens that you cannot understand the word you looked up because there are still some words you do not understand in its definitions or explanations provided by the dictionary. You can keep looking up these new words recursively till they all can be well explained by some basic words you already knew. How to automatically find a minimum set of such basic words to define (or recursively define) the entire vocabulary in a given dictionary is what are going to discuss in this paper. We propose an efficient algorithm to construct the Minimum Vocabulary (MV) using the word frequency information. The minimum vocabulary can be used for language modeling and experimental results demonstrate the effectiveness of using the minimum vocabulary as features in text classification. © 2012 Springer-Verlag.

关键词： Classification (of information)

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Hartmann, Valentin N. Orthey, Andreas Driess, Danny Oguz, Ozgur S. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Group TU Berlin Germany Department of Computer Engineering Bilkent University Turkey

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multirobot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world. Copyright © 2021, The Authors. All rights reserved.

关键词： robotics

Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon

学校读者我要写书评

暂无评论

Multi-Step Model Predictive Safety Filters: Reducing Chatter...

IEEE Conference on Decision and Control

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

learning-based controllers have demonstrated su-perior performance compared to classical controllers in various tasks. However, providing safety guarantees is not trivial. Safety, the satisfaction of state and input constraints, can be guaranteed by augmenting the learned control policy with a safety filter. Model predictive safety filters (MPSFs) are a common safety filtering approach based on model predictive control (MPC). MPSFs seek to guarantee safety while minimizing the difference between the proposed and applied inputs in the immediate next time step. This limited foresight can lead to jerky motions and undesired oscillations close to constraint boundaries, known as chattering. In this paper, we reduce chattering by considering input corrections over a longer horizon. Under the assumption of bounded model uncertainties, we prove recursive feasibility using techniques from robust MPC. We verified the proposed approach in both extensive simulation and quadrotor exper-iments. In experiments with a Crazyflie 2.0 drone, we show that, in addition to preserving the desired safety guarantees, the proposed MPSF reduces chattering by more than a factor of 4 compared to previous MPSF formulations.

关键词：

learning Adaptive Multi-Objective Robot Navigation Incorporating Demonstrations

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Heuvel, Jorge de Sethuraman, Tharun Bennewitz, Maren The Humanoid Robots Lab University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn Germany

Preference-aligned robot navigation in human environments is typically achieved through learning-based approaches, utilizing user feedback or demonstrations for personalization. However, personal preferences are subject to change and might even be context-dependent. Yet traditional reinforcement learning (RL) approaches with static reward functions often fall short in adapting to these varying user preferences, inevitably reflecting demonstrations once training is completed. This paper introduces a framework that combines multiobjective reinforcement learning (MORL) with demonstration-based learning. Our approach allows for dynamic adaptation to changing user preferences without retraining. It fluently modulates between reward-defined preference objectives and the amount of demonstration data reflection. Through rigorous evaluations, including a sim-to-real transfer on two robots, we demonstrate our framework’s capability to reflect user preferences accurately while achieving high navigational performance in terms of collision avoidance and goal pursuance. © 2024, CC BY.

关键词： Adversarial machine learning

EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： de Heuvel, Jorge Seiler, Florian Bennewitz, Maren The Humanoid Robots Lab The Center for Robotics The University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany

To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigation trajectory suggestions for a given navigation task. In this paper, we introduce EnQuery, a query generation approach using an ensemble of policies that achieve behavioral diversity through a regularization term. For a given navigation task, EnQuery produces multiple navigation trajectory suggestions, thereby optimizing the efficiency of preference data collection with fewer queries. Our methodology demonstrates superior performance in aligning navigation policies with user preferences in low-query regimes, offering enhanced policy convergence from sparse preference queries. The evaluation is complemented with a novel explainability representation, capturing full scene navigation behavior of the mobile robot in a single plot. Our code is available online at https://***/hrl-bonn/EnQuery. © 2024, CC BY.

关键词： Navigation

Motion prediction with recurrent neural network dynamical models and trajectory optimization

学校读者我要写书评

暂无评论

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

Predicting human motion in unstructured and dynamic environments 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 to encode the lower level aspects of human motion separately from the higher level geometrical aspects, which we believe will generalize better over environments. In contrast to our prior work [6], we encode the short-term behavior by using a state-of-the-art recurrent neural network structure instead of a Gaussian process. In order to perform longer term behavior predictions that account for variation in tasks and environments, we propose to make use of gradient based trajectory optimization. Preliminary experiments on real motion data demonstrate the efficacy of the approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation

学校读者我要写书评

暂无评论

EnQuery: Ensemble Policies for Diverse Query-Generation in P...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jorge De Heuvel Florian Seiler Maren Bennewitz Humanoid Robots Lab and the Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

关键词： Training Navigation Data visualization Reinforcement learning Reliability engineering Trajectory Behavioral sciences Recycling Mobile robots Data mining