Zobrazeno 1 - 10
of 222
pro vyhledávání: '"Mittal, Mayank"'
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a systematic approach to behavior synthesis and control for multi-contact lo
Externí odkaz:
http://arxiv.org/abs/2410.13817
Combining manipulation with the mobility of legged robots is essential for a wide range of robotic applications. However, integrating an arm with a mobile base significantly increases the system's complexity, making precise end-effector control chall
Externí odkaz:
http://arxiv.org/abs/2409.16048
Pedipulation leverages the feet of legged robots for mobile manipulation, eliminating the need for dedicated robotic arms. While previous works have showcased blind and task-specific pedipulation skills, they fail to account for static and dynamic ob
Externí odkaz:
http://arxiv.org/abs/2409.07195
Autor:
Yu, Qinxi, Moghani, Masoud, Dharmarajan, Karthik, Schorp, Vincent, Panitch, William Chung-Ho, Liu, Jingzhou, Hari, Kush, Huang, Huang, Mittal, Mayank, Goldberg, Ken, Garg, Animesh
Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment remains a challenge. In this paper, we present ORBIT-Surgical, a physic
Externí odkaz:
http://arxiv.org/abs/2404.16027
Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively. Often, the learned behaviors fail to achieve the desired transformat
Externí odkaz:
http://arxiv.org/abs/2403.04359
Legged robots have the potential to become vital in maintenance, home support, and exploration scenarios. In order to interact with and manipulate their environments, most legged robots are equipped with a dedicated robot arm, which means additional
Externí odkaz:
http://arxiv.org/abs/2402.10837
Real-time path planning in outdoor environments still challenges modern robotic systems due to differences in terrain traversability, diverse obstacles, and the necessity for fast decision-making. Established approaches have primarily focused on geom
Externí odkaz:
http://arxiv.org/abs/2310.00982
Publikováno v:
2023 International Conference on Robotics and Automation
When humans perform a task with an articulated object, they interact with the object only in a handful of ways, while the space of all possible interactions is nearly endless. This is because humans have prior knowledge about what interactions are li
Externí odkaz:
http://arxiv.org/abs/2305.17565
Autor:
Mittal, Mayank, Yu, Calvin, Yu, Qinxi, Liu, Jingzhou, Rudin, Nikita, Hoeller, David, Yuan, Jia Lin, Singh, Ritvik, Guo, Yunrong, Mazhar, Hammad, Mandlekar, Ajay, Babich, Buck, State, Gavriel, Hutter, Marco, Garg, Animesh
Publikováno v:
IEEE Robotics and Automation Letters (Volume: 8, Issue: 6, June 2023)
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body
Externí odkaz:
http://arxiv.org/abs/2301.04195
In this paper, we present a real-time whole-body planner for collision-free legged mobile manipulation. We enforce both self-collision and environment-collision avoidance as soft constraints within a Model Predictive Control (MPC) scheme that solves
Externí odkaz:
http://arxiv.org/abs/2202.12385