Human demonstrations are a scalable data source for learning dexterous manipulation, but the embodiment gap prevents human motion from being executed directly on robots. Inverse kinematics (IK) retargets human motion to robots efficiently but ignores dynamics, often producing infeasible motions. Reinforcement learning...
De-Chen Gao, Yue Yang, Ben Abbatematteo et al.· 0 citations
HALTER, a Harness for Autonomous Long-horizon Task Evaluation and Reset, which restores the scene by planning over a library of learned atomic reset skills, so demonstration cost scales with the size of that library rather than with the number of terminal states.
Jing Jiang, Yue Yang, Xin-Kai Jiang et al.· 0 citations
Evidence-Gated Regularization (EGR) is proposed, a modality-agnostic training objective that introduces zero inference-time overhead and derives a per-frame and per-sensor task-relevance signal to gate two state-conditional consistency objectives: invariance on low-evidence sensors, and single-sensor sufficiency on hig...
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.· arXiv.org· 1 citation
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