Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Huang, Zhiao"'
Imitation learning (IL) enables agents to acquire skills directly from expert demonstrations, providing a compelling alternative to reinforcement learning. However, prior online IL approaches struggle with complex tasks characterized by high-dimensio
Externí odkaz:
http://arxiv.org/abs/2410.14081
Autor:
Tao, Stone, Xiang, Fanbo, Shukla, Arth, Qin, Yuzhe, Hinrichsen, Xander, Yuan, Xiaodi, Bao, Chen, Lin, Xinsong, Liu, Yulin, Chan, Tse-kai, Gao, Yuan, Li, Xuanlin, Mu, Tongzhou, Xiao, Nan, Gurha, Arnav, Huang, Zhiao, Calandra, Roberto, Chen, Rui, Luo, Shan, Su, Hao
Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks and lack features critical for scaling generalizable robotics and sim2re
Externí odkaz:
http://arxiv.org/abs/2410.00425
Combining gradient-based trajectory optimization with differentiable physics simulation is an efficient technique for solving soft-body manipulation problems. Using a well-crafted optimization objective, the solver can quickly converge onto a valid t
Externí odkaz:
http://arxiv.org/abs/2312.06408
Autor:
Liang, Litian, Bian, Liuyu, Xiao, Caiwei, Zhang, Jialin, Chen, Linghao, Liu, Isabella, Xiang, Fanbo, Huang, Zhiao, Su, Hao
Building robots that can automate labor-intensive tasks has long been the core motivation behind the advancements in computer vision and the robotics community. Recent interest in leveraging 3D algorithms, particularly neural fields, has led to advan
Externí odkaz:
http://arxiv.org/abs/2312.06686
We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy that overcomes limitations inherent in the commonly-used Gaussian par
Externí odkaz:
http://arxiv.org/abs/2307.10710
Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks. While CoT allows models to produce more comprehensive reasoning processes, its emphasis on intermediate reasoning steps ca
Externí odkaz:
http://arxiv.org/abs/2306.03872
We study generalizable policy learning from demonstrations for complex low-level control (e.g., contact-rich object manipulations). We propose a novel hierarchical imitation learning method that utilizes sub-optimal demos. Firstly, we propose an obse
Externí odkaz:
http://arxiv.org/abs/2304.00776
In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interacti
Externí odkaz:
http://arxiv.org/abs/2304.03223
We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on the same part share the common motion pattern. From the perspective o
Externí odkaz:
http://arxiv.org/abs/2303.05703
We introduce RoboNinja, a learning-based cutting system for multi-material objects (i.e., soft objects with rigid cores such as avocados or mangos). In contrast to prior works using open-loop cutting actions to cut through single-material objects (e.
Externí odkaz:
http://arxiv.org/abs/2302.11553