1 citations · 1 across the 5 of their papers we have counts for
6 papers
HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface
Zimu Han, Yiming Zeng, Jiyao Zhang +9
Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuni…
UniTac2Pose: A Unified Approach Learned in Simulation for Category-level Visuotactile In-hand Pose Estimation
Mingdong Wu, Long Yang, Jin Liu +5
Accurate estimation of the in-hand pose of an object based on its CAD model is crucial in both industrial applications and everyday tasks, ranging from positioning workpieces and a…
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.…
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…
AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning
Yuanfei Wang, Xiaojie Zhang, Ruihai Wu +6
Articulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated ob…
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World
Yankai Fu, Qiuxuan Feng, Ning Chen +8
Achieving human-level dexterity in robots is a key objective in the field of robotic manipulation. Recent advancements in 3D-based imitation learning have shown promising results,…