103 citations · 188 across the 27 of their papers we have counts for
7 papers · 1 filter
SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution
Zhixuan Liang, Yao Mu, Hengbo Ma +3
Diffusion models have demonstrated strong potential for robotic trajectory planning. However, generating coherent trajectories from high-level instructions remains challenging, esp…
Human-oriented Representation Learning for Robotic Manipulation
Mingxiao Huo, Mingyu Ding, Chenfeng Xu +6
Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We advocate that s…
Generalizable Long-Horizon Manipulations with Large Language Models
Haoyu Zhou, Mingyu Ding, Weikun Peng +3
This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with…
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving
Tong Zhao, Chenfeng Xu, Mingyu Ding +3
This paper addresses the growing demands for safety and comfort in intelligent robot systems, particularly autonomous vehicles, where road conditions play a pivotal role in overall…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
Mengkang Hu, Yao Mu, Xinmiao Yu +7
This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on…
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
Hao Sha, Yao Mu, Yuxuan Jiang +7
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To add…