4 citations · 9 across the 8 of their papers we have counts for
11 papers · 1 filter
-WM: A Unified Video-Action World Model for Robotic Manipulation
Pengfei Zhou, Shengcong Chen, Di Chen +17
Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present -World…
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Yi Wang, Xinchen Li, Pengwei Xie +13
Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter dist…
SOP: A Scalable Online Post-Training System for Vision-Language-Action Models
Mingjie Pan, Siyuan Feng, Qinglin Zhang +9
Vision-language-action (VLA) models achieve strong generalization through large-scale pre-training, but real-world deployment requires expert-level task proficiency in addition to…
Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation
Yunhai Feng, Jiaming Han, Zhuoran Yang +3
Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities, the ability to reason about the physical world, and reactively c…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
Jianlan Luo, Charles Xu, Jeffrey Wu +1
Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has…