Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Tateo, Davide"'
Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods attempt to address this issue, they often struggle to learn human-like gai
Externí odkaz:
http://arxiv.org/abs/2407.11658
Planning robot contact often requires reasoning over a horizon to anticipate outcomes, making such planning problems computationally expensive. In this letter, we propose a learning framework for efficient contact planning in real-time subject to unc
Externí odkaz:
http://arxiv.org/abs/2407.03705
Autor:
Mower, Christopher E., Wan, Yuhui, Yu, Hongzhan, Grosnit, Antoine, Gonzalez-Billandon, Jonas, Zimmer, Matthieu, Wang, Jinlong, Zhang, Xinyu, Zhao, Yao, Zhai, Anbang, Liu, Puze, Palenicek, Daniel, Tateo, Davide, Cadena, Cesar, Hutter, Marco, Peters, Jan, Tian, Guangjian, Zhuang, Yuzheng, Shao, Kun, Quan, Xingyue, Hao, Jianye, Wang, Jun, Bou-Ammar, Haitham
We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our system integrates large language models (LLMs), enabling non-experts to a
Externí odkaz:
http://arxiv.org/abs/2406.19741
Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. However, most existing approaches are trained in well-tuned simulators and subsequently de
Externí odkaz:
http://arxiv.org/abs/2404.09080
Autor:
Lach, Luca, Haschke, Robert, Tateo, Davide, Peters, Jan, Ritter, Helge, Borràs, Júlia, Torras, Carme
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks. An important line of research in this regard is that of grasp for
Externí odkaz:
http://arxiv.org/abs/2311.07245
Stateful policies play an important role in reinforcement learning, such as handling partially observable environments, enhancing robustness, or imposing an inductive bias directly into the policy structure. The conventional method for training state
Externí odkaz:
http://arxiv.org/abs/2311.04082
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents. However, many existing locomotion benchmarks primarily focus on simplified toy tasks, often failing to capture the complexity of real-world scenarios and st
Externí odkaz:
http://arxiv.org/abs/2311.02496
Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function. However, these methods generally require implicit reward regularization to improve stability and often m
Externí odkaz:
http://arxiv.org/abs/2303.00599
Autor:
Kicki, Piotr, Liu, Puze, Tateo, Davide, Bou-Ammar, Haitham, Walas, Krzysztof, Skrzypczyński, Piotr, Peters, Jan
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning. However, when dynamic motions under constraints are needed
Externí odkaz:
http://arxiv.org/abs/2301.04330
Autor:
Bjelonic, Filip, Lee, Joonho, Arm, Philip, Sako, Dhionis, Tateo, Davide, Peters, Jan, Hutter, Marco
Publikováno v:
IEEE Robotics and Automation Letters (2023)
Parallel-elastic joints can improve the efficiency and strength of robots by assisting the actuators with additional torques. For these benefits to be realized, a spring needs to be carefully designed. However, designing robots is an iterative and te
Externí odkaz:
http://arxiv.org/abs/2301.03509