Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Jin, Wanxin"'
Learning from Demonstrations, particularly from biological experts like humans and animals, often encounters significant data acquisition challenges. While recent approaches leverage internet videos for learning, they require complex, task-specific p
Externí odkaz:
http://arxiv.org/abs/2410.09286
Autor:
Yang, Wen, Jin, Wanxin
In this paper, we propose ContactSDF, a method that uses signed distance functions (SDFs) to approximate multi-contact models, including both collision detection and time-stepping routines. ContactSDF first establishes an SDF using the supporting pla
Externí odkaz:
http://arxiv.org/abs/2408.09612
Autor:
Jin, Wanxin
A significant barrier preventing model-based methods from matching the high performance of reinforcement learning in dexterous manipulation is the inherent complexity of multi-contact dynamics. Traditionally formulated using complementarity models, m
Externí odkaz:
http://arxiv.org/abs/2408.07855
A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and constraints from demonstrations. Different from existing work, where
Externí odkaz:
http://arxiv.org/abs/2407.19902
In safety-critical robot planning or control, manually specifying safety constraints or learning them from demonstrations can be challenging. In this paper, we propose a certifiable alignment method for a robot to learn a safety constraint in its mod
Externí odkaz:
http://arxiv.org/abs/2407.04216
This paper presents a solution to address carbon emission mitigation for end-to-end edge computing systems, including the computing at battery-powered edge devices and servers, as well as the communications between them. We design and implement, Carb
Externí odkaz:
http://arxiv.org/abs/2404.16970
Autor:
Zhang, Shenao, Zheng, Sirui, Ke, Shuqi, Liu, Zhihan, Jin, Wanxin, Yuan, Jianbo, Yang, Yingxiang, Yang, Hongxia, Wang, Zhaoran
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorithms may require extensive trial-and-error interactions to collect usef
Externí odkaz:
http://arxiv.org/abs/2402.16181
The hybrid nature of multi-contact robotic systems, due to making and breaking contact with the environment, creates significant challenges for high-quality control. Existing model-based methods typically rely on either good prior knowledge of the mu
Externí odkaz:
http://arxiv.org/abs/2310.09893
The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying the driving styles of its nearby AVs can reliably evaluate the risk of c
Externí odkaz:
http://arxiv.org/abs/2308.12069
Autor:
Jin, Wanxin, Posa, Michael
In contact-rich tasks, like dexterous manipulation, the hybrid nature of making and breaking contact creates challenges for model representation and control. For example, choosing and sequencing contact locations for in-hand manipulation, where there
Externí odkaz:
http://arxiv.org/abs/2211.16657