Author

Mingyu Ding

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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

Jun 2026

AnyBody: Free-Form Whole-Body Humanoid Control from Arbitrary Keypoint Guidance

The effectiveness of Any body, a unified whole-body humanoid controller driven by an arbitrary subset of body keypoints chosen at deploy time, is demonstrated by tracking large-scale human motions from arbitrary keypoint subsets, free-form control, flexibly teleoperating, and learning downstream behaviors including locomotion, in-air writing, and obstacle-reach.

Shuning Li, Sikai Li, Jiachen Li et al. · arXiv.org · 1 citation