activity
20242026
collaborators

6 papers

cs.RO2026

Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion

Zhuo Li, Junjia Liu, Zhipeng Dong +4

Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies still suffer from substantial perf…

cs.RO2026

Disentangling perception and reasoning for improving data efficiency in learning cloth manipulation without demonstrations

Donatien Delehelle, Fei Chen, Darwin Caldwell

Cloth manipulation is a ubiquitous task in everyday life, but it remains an open challenge for robotics. The difficulties in developing cloth manipulation policies are attributed t…

cs.RO2025

CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics

Zixin Tang, Yiming Chen, Quentin Rouxel +3

Teleoperation presents a promising paradigm for remote control and robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from li…

cs.RO2025

A Hyperspectral Imaging Guided Robotic Grasping System

Zheng Sun, Zhipeng Dong, Shixiong Wang +2

Hyperspectral imaging is an advanced technique for precisely identifying and analyzing materials or objects. However, its integration with robotic grasping systems has so far been…

cs.RO2025

ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation

Zhuo Li, Junjia Liu, Dianxi Li +5

Recent work has demonstrated the potential of diffusion models in robot bimanual skill learning. However, existing methods ignore the learning of posture-dependent task features, w…

cs.RO2024

Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

Junjia Liu, Zhuo Li, Minghao Yu +4

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuo…