2 citations · 2 across the 7 of their papers we have counts for
7 papers
Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models
Yuhan Liu, Xinyu Zhang, Litao Liu +1
Vision-Language-Action (VLA) policies offer strong general-purpose manipulation priors, but often fail on tight-tolerance, contact-rich assembly due to long-horizon credit assignme…
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…
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…
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…
DAP: Diffusion-based Affordance Prediction for Multi-modality Storage
Haonan Chang, Kowndinya Boyalakuntla, Yuhan Liu +3
Solving storage problem: where objects must be accurately placed into containers with precise orientations and positions, presents a distinct challenge that extends beyond traditio…
A3VLM: Actionable Articulation-Aware Vision Language Model
Siyuan Huang, Haonan Chang, Yuhan Liu +5
Vision Language Models (VLMs) have received significant attention in recent years in the robotics community. VLMs are shown to be able to perform complex visual reasoning and scene…
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…