Peterson Co — accepted AI-conference papers

2026

Action-Sketcher: From Reasoning to Action via Visual Sketches for Robotic Manipulation

Huajie Tan, Peterson Co, Yijie Xu, Shanyu Rong, Yuheng Ji, Cheng Chi +5

CVPR 2026

Long-horizon robotic manipulation is increasingly important for real-world deployment, requiring spatial disambiguation in complex layouts and temporal resilience under dynamic interaction. However, existing end-to-end and hierarchical Vision-Language-Action (VLA) policies often rely on text-only cu

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2026

General Process Reward Modeling for Robotic Reinforcement Learning

Huajie Tan, Sixiang Chen, Yijie Xu, Zixiao Wang, Cheng Chi, Yuheng Ji +9

CVPR 2026

The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promising direction, they are often hindered by two fundamental limitations: their reward models lack ste

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