6 papers
Motion Blender Gaussian Splatting for Dynamic Scene Reconstruction
Xinyu Zhang, Haonan Chang, Yuhan Liu +1
Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes. However, existing methods primarily rely on implicit motion representations, s…
Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies
Yuhan Liu, Xinyu Zhang, Haonan Chang +1
This paper addresses the challenges of Rhythmic Insertion Tasks (RIT), where a robot must repeatedly perform high-precision insertions, such as screwing a nut into a bolt with a wr…
Autoregressive Action Sequence Learning for Robotic Manipulation
Xinyu Zhang, Yuhan Liu, Haonan Chang +2
Designing a universal policy architecture that performs well across diverse robots and task configurations remains a key challenge. In this work, we address this by representing ro…
UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models
Qiaojun Yu, Siyuan Huang, Xibin Yuan +9
Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances. To address these challenges, we propose a com…
Scaling Manipulation Learning with Visual Kinematic Chain Prediction
Xinyu Zhang, Yuhan Liu, Haonan Chang +1
Learning general-purpose models from diverse datasets has achieved great success in machine learning. In robotics, however, existing methods in multi-task learning are typically co…
LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement
Haonan Chang, Kai Gao, Kowndinya Boyalakuntla +5
We introduce a novel approach to the executable semantic object rearrangement problem. In this challenge, a robot seeks to create an actionable plan that rearranges objects within…