Preprint
Jul 2026
DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation
Experiments show that DenseReward outperforms general-purpose VLMs and existing robotic reward models in dense reward prediction across both simulated and real-world manipulation, and provides effective reward guidance for downstream model predictive control and reinforcement learning.
Yu Fang, Wanxi Dong, Jiaqi Liu et al.
· 1 citation