6 papers
CADGrasp: Learning Contact and Collision Aware General Dexterous Grasping in Cluttered Scenes
Jiyao Zhang, Zhiyuan Ma, Tianhao Wu +2
Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands, occlusion, and potential collisions arising from…
ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
Zeyuan Chen, Qiyang Yan, Yuanpei Chen +6
Dexterous grasping in cluttered scenes presents significant challenges due to diverse object geometries, occlusions, and potential collisions. Existing methods primarily focus on s…
Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation
Jinzhou Li, Tianhao Wu, Jiyao Zhang +6
Effectively utilizing multi-sensory data is important for robots to generalize across diverse tasks. However, the heterogeneous nature of these modalities makes fusion challenging.…
Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning
Tianhao Wu, Jinzhou Li, Jiyao Zhang +2
Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on de…
GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
Tianhao Wu, Mingdong Wu, Jiyao Zhang +2
The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper…
Boosting Universal LLM Reward Design through Heuristic Reward Observation Space Evolution
Zen Kit Heng, Zimeng Zhao, Tianhao Wu +4
Large Language Models (LLMs) are emerging as promising tools for automated reinforcement learning (RL) reward design, owing to their robust capabilities in commonsense reasoning an…