Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Tan, Junbo"'
Autor:
Cao, Chenyang, Xin, Yucheng, Wu, Silang, He, Longxiang, Yan, Zichen, Tan, Junbo, Wang, Xueqian
Model-based Reinforcement Learning (RL) has shown its high training efficiency and capability of handling high-dimensional tasks. Regarding safety issues, safe model-based RL can achieve nearly zero-cost performance and effectively manage the trade-o
Externí odkaz:
http://arxiv.org/abs/2407.04942
Implicit Q-learning (IQL) serves as a strong baseline for offline RL, which learns the value function using only dataset actions through quantile regression. However, it is unclear how to recover the implicit policy from the learned implicit Q-functi
Externí odkaz:
http://arxiv.org/abs/2405.18187
Offline goal-conditioned reinforcement learning (GCRL) aims at solving goal-reaching tasks with sparse rewards from an offline dataset. While prior work has demonstrated various approaches for agents to learn near-optimal policies, these methods enco
Externí odkaz:
http://arxiv.org/abs/2403.01734
Whether a PTAS (polynomial-time approximation scheme) exists for game equilibria has been an open question, and its absence has indications and consequences in three fields: the practicality of methods in algorithmic game theory, non-stationarity and
Externí odkaz:
http://arxiv.org/abs/2401.00747
Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR). However, previous methods may still encounter out-of-distribution actions due to the limited
Externí odkaz:
http://arxiv.org/abs/2310.05333
Autonomous terrain traversal of articulated tracked robots can reduce operator cognitive load to enhance task efficiency and facilitate extensive deployment. We present a novel hybrid trajectory optimization method aimed at generating efficient, stab
Externí odkaz:
http://arxiv.org/abs/2306.02659
Autor:
Li, Shoujie, He, Mingshan, Ding, Wenbo, Ye, Linqi, Wang, Xueqian, Tan, Junbo, Yuan, Jinqiu, Zhang, Xiao-Ping
Manual oropharyngeal (OP) swab sampling is an intensive and risky task. In this article, a novel OP swab sampling device of low cost and high compliance is designed by combining the visuo-tactile sensor and the pneumatic actuator-based gripper. Here,
Externí odkaz:
http://arxiv.org/abs/2305.06537
Given the recent surge of interest in data-driven control, this paper proposes a two-step method to study robust data-driven control for a parameter-unknown linear time-invariant (LTI) system that is affected by energy-bounded noises. First, two data
Externí odkaz:
http://arxiv.org/abs/2203.06959
For a parameter-unknown linear descriptor system, this paper proposes data-driven methods to testify the system's type and controllability and then to stabilize it. First, a data-based condition is developed to identify whether this unknown system is
Externí odkaz:
http://arxiv.org/abs/2112.03665
Publikováno v:
In Acta Astronautica October 2023 211:257-267