103 citations · 188 across the 27 of their papers we have counts for
8 papers · 2 filters
DexH2R: Task-oriented Dexterous Manipulation from Human to Robots
Shuqi Zhao, Xinghao Zhu, Yuxin Chen +5
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and…
Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning
Jiaqi Liu, Chengkai Xu, Peng Hang +4
The cooperative driving technology of Connected and Autonomous Vehicles (CAVs) is crucial for improving the efficiency and safety of transportation systems. Learning-based methods,…
Embodiment-Agnostic Action Planning via Object-Part Scene Flow
Weiliang Tang, Jia-Hui Pan, Wei Zhan +6
Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the…
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
Shiyu Fang, Jiaqi Liu, Mingyu Ding +4
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not sat…
P2 Explore: Efficient Exploration in Unknown Cluttered Environment with Floor Plan Prediction
Kun Song, Gaoming Chen, Masayoshi Tomizuka +3
Robot exploration aims at the reconstruction of unknown environments, and it is important to achieve it with shorter paths. Traditional methods focus on optimizing the visiting ord…
Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning
Yixiao Wang, Yifei Zhang, Mingxiao Huo +8
The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning. Traditional models typically rely on a universal policy for all ta…
DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models
Yuanfei Lin, Chenran Li, Mingyu Ding +3
Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature…
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
Dingkun Guo, Yuqi Xiang, Shuqi Zhao +4
Robotic grasping is a fundamental aspect of robot functionality, defining how robots interact with objects. Despite substantial progress, its generalizability to counter-intuitive…