Zobrazeno 1 - 10
of 1 823
pro vyhledávání: '"ZHAO, Ding"'
Autor:
Feng, Yuming, Hong, Chuye, Niu, Yaru, Liu, Shiqi, Yang, Yuxiang, Yu, Wenhao, Zhang, Tingnan, Tan, Jie, Zhao, Ding
Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescu
Externí odkaz:
http://arxiv.org/abs/2411.07104
Autor:
Zhang, Xilun, Liu, Shiqi, Huang, Peide, Han, William Jongwon, Lyu, Yiqi, Xu, Mengdi, Zhao, Ding
Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies between simulated and real-world dynamics. Traditional methods like Domain Randomization often fail to capture fine-grained dynamics, limiting their effectivene
Externí odkaz:
http://arxiv.org/abs/2410.20357
The development of vision-based tactile sensors has significantly enhanced robots' perception and manipulation capabilities, especially for tasks requiring contact-rich interactions with objects. In this work, we present DTactive, a novel vision-base
Externí odkaz:
http://arxiv.org/abs/2410.08337
As information interaction technology advances, the efficiency, dimensionality, and user experience of information transmission have significantly improved. Communication has evolved from letters to telegraphs, markedly increasing transmission speed;
Externí odkaz:
http://arxiv.org/abs/2409.14487
Autor:
Yang, Yuxiang, Shi, Guanya, Lin, Changyi, Meng, Xiangyun, Scalise, Rosario, Castro, Mateo Guaman, Yu, Wenhao, Zhang, Tingnan, Zhao, Ding, Tan, Jie, Boots, Byron
We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over l
Externí odkaz:
http://arxiv.org/abs/2409.10923
Autor:
Yao, Yihang, Cen, Zhepeng, Ding, Wenhao, Lin, Haohong, Liu, Shiqi, Zhang, Tingnan, Yu, Wenhao, Zhao, Ding
Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using a pre-collected dataset. Most current methods struggle with the mismatch between imperfect demonstrations and the desired safe and rewarding performance.
Externí odkaz:
http://arxiv.org/abs/2407.14653
BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies, especially in scenarios where exploration is costly or infeasible. Nevertheless, its performance often suffer
Externí odkaz:
http://arxiv.org/abs/2407.10967
In the field of safe reinforcement learning (RL), finding a balance between satisfying safety constraints and optimizing reward performance presents a significant challenge. A key obstacle in this endeavor is the estimation of safety constraints, whi
Externí odkaz:
http://arxiv.org/abs/2405.11718
Autor:
Dalrymple, David "davidad", Skalse, Joar, Bengio, Yoshua, Russell, Stuart, Tegmark, Max, Seshia, Sanjit, Omohundro, Steve, Szegedy, Christian, Goldhaber, Ben, Ammann, Nora, Abate, Alessandro, Halpern, Joe, Barrett, Clark, Zhao, Ding, Zhi-Xuan, Tan, Wing, Jeannette, Tenenbaum, Joshua
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper
Externí odkaz:
http://arxiv.org/abs/2405.06624
We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method named $Meta$-$Evolve$ that uses continuous robot evolution to efficiently transfer the policy to
Externí odkaz:
http://arxiv.org/abs/2405.03534