7 papers
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
Ju Dong, Liding Zhang, Lei Zhang +7
Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on…
ResponsibleRobotBench: Benchmarking Responsible Robot Manipulation using Multi-modal Large Language Models
Lei Zhang, Ju Dong, Kaixin Bai +4
Recent advances in large multimodal models have enabled new opportunities in embodied AI, particularly in robotic manipulation. These models have shown strong potential in generali…
OmniDexVLG: Learning Dexterous Grasp Generation from Vision Language Model-Guided Grasp Semantics, Taxonomy and Functional Affordance
Lei Zhang, Diwen Zheng, Kaixin Bai +5
Dexterous grasp generation aims to produce grasp poses that align with task requirements and human interpretable grasp semantics. However, achieving semantically controllable dexte…
M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation
Ju Dong, Lei Zhang, Liding Zhang +8
Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. E…
DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
Lei Zhang, Soumya Mondal, Zhenshan Bing +5
Dexterous robotic manipulation remains a longstanding challenge in robotics due to the high dimensionality of control spaces and the semantic complexity of object interaction. In t…
Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy
Minheng Ni, Lei Zhang, Zihan Chen +4
Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks, such as poisonings, fires, and even explosions. In this paper, we present respon…