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检索条件"机构=Machine Learning and Robotics Lab University of Stuttgart"
148 条 记 录,以下是31-40 订阅
排序:
Constrained Bayesian optimization of combined interaction force/task space controllers for manipulations
Constrained Bayesian optimization of combined interaction fo...
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IEEE International Conference on robotics and Automation (ICRA)
作者: Danny Drieß Peter Englert Marc Toussaint Universitat Stuttgart Stuttgart Baden-Württemberg DE Machine Learning and Robotics Lab University of Stuttgart Germany
In this paper, we address the problem of how a robot can optimize parameters of combined interaction force/task space controllers under a success constraint in an active way. To enable the robot to explore its environ... 详细信息
来源: 评论
A Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty
A Probabilistic Framework for Constrained Manipulations and ...
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IEEE International Conference on robotics and Automation (ICRA)
作者: Jung-Su Ha Danny Driess Marc Toussaint Machine Learning & Robotics Lab University Stuttgart and with the Max Planck Institute for Intelligent Systems Stuttgart Germany
Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit... 详细信息
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Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty
arXiv
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arXiv 2020年
作者: Ha, Jung-Su Driess, Danny Toussaint, Marc Machine Learning & Robotics Lab University Stuttgart Max Planck Institute for Intelligent Systems Stuttgart Germany
— Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, ... 详细信息
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Robot programming from demonstration, feedback and transfer
Robot programming from demonstration, feedback and transfer
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IEEE/RSJ International Conference on Intelligent Robots and Systems
作者: Y. Mollard T. Munzer A. Baisero M. Toussaint M. Lopes Flowers Team Inria France Machine Learning and Robotics Lab University of Stuttgart Germany
This paper presents a novel approach for robot instruction for assembly tasks. We consider that robot programming can be made more efficient, precise and intuitive if we leverage the advantages of complementary approa... 详细信息
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ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time
arXiv
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arXiv 2022年
作者: Grothe, Francesco Hartmann, Valentin N. Orthey, Andreas Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Machine Learning & Robotics Lab University of Stuttgart Germany
We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectio... 详细信息
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learning to execute: efficiently learning universal plan-conditioned policies in robotics  21
Learning to execute: efficiently learning universal plan-con...
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Proceedings of the 35th International Conference on Neural Information Processing Systems
作者: Ingmar Schubert Danny Driess Ozgur S. Oguz Marc Toussaint Learning and Intelligent Systems Group TU Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany
Applications of Reinforcement learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like...
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learning to execute: Efficiently learning universal plan-conditioned policies in robotics
arXiv
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arXiv 2021年
作者: Schubert, Ingmar Driess, Danny Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group Tu Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany
Applications of Reinforcement learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like... 详细信息
来源: 评论
Deep Visual Heuristics: learning Feasibility of Mixed-Integer Programs for Manipulation Planning
Deep Visual Heuristics: Learning Feasibility of Mixed-Intege...
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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
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 challen... 详细信息
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Escaping local optima in POMDP planning as inference
Escaping local optima in POMDP planning as inference
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10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011
作者: Poupart, Pascal Lang, Tobias Toussaint, Marc David R. Cheriton School of Computer Science University of Waterloo ON Canada Machine Learning and Robotics Lab. FU Berlin Germany
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Mechanisms of Social learning in Evolved Artificial Life
Mechanisms of Social Learning in Evolved Artificial Life
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2020 Conference on Artificial Life, ALIFE 2020
作者: Bartoli, Alberto Catto, Marco De Lorenzo, Andrea Medvet, Eric Talamini, Jacopo Machine Learning Lab. Department of Engineering and Architecture University of Trieste Italy Evolutionary Robotics and Artificial Life Lab. Department of Engineering and Architecture University of Trieste Italy
Adaptation of agents in artificial life scenarios is especially effective when agents may evolve, i.e., inherit traits from their parents, and learn by interacting with the environment. The learning process may be boo... 详细信息
来源: 评论