A LLM-based DFG framework that can synthesize versatile dexterous functional grasps from language instructions and achieve open-set generalization on novel functional concepts.

A transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype and bypassing the need for retraining on every new humanoid platform

A reachability-aware multi-fingered grasp planning framework for humanoid manipulation that can synthesize feasible high-DOF grasps for novel objects in unrestricted workspace.

An integrated scheme for solving the path-wise IK problem of a 7-DoF AMREO in the position domain.

An novel plug-and-play inference-time policy steering method that enables zero-shot deployment of pre-trained VLA policies without any additional fine-tuning or data collection.

We propose Manipulability-Aware Diffusion Policy (ManiDP), a novel imitation learning method that not only generates plausible bimanual trajectories, but also optimizes dual-arm configurations to better satisfy posture-dependent task requirements.

A generalizable framework that combines a Large Language Model, a Visual-language Model, and symbolic planning to address long-horizon bimanual mobile manipulation tasks.