dense reward learning 1failure synthesis 1reinforcement learning 1robotic manipulation 1vision-language models 1
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cs.RO2026
DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation
Yu Fang, Wanxi Dong, Jiaqi Liu +7
The paper presents DenseReward, a dense visual‑language reward model for robotic manipulation that is trained on automatically synthesized failure trajectories in simulation, enabl…
cs.RO2026
Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving
Yuhong Shi, Jianyi Liu, Lihang Sun +2
With the evolution of large foundation models (LFMs), data-driven autonomous driving has made significant strides. However, existing paradigms still face severe challenges in compl…
cs.RO2024
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
Yanjie Ze, Ge Yan, Yueh-Hua Wu +6
It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To ach…