ARM: Advantage Reward Modeling for Long-Horizon Manipulation
Long-horizon robotic manipulation remains challenging for reinforcement learning (RL) because sparse rewards provide limited guidance for credit assignment. Practical policy improvement thus relies on richer intermediate supervision, such as dense progress rewards, which are costly to obtain and ill-suited to non-monotonic behaviors such as backtracking and recovery. To address this, we propose Advantage Reward Modeling (ARM), a framework that shifts from hard-to-quantify absolute progress to estimating relative advantage. We introduce a cost-effective tri-state labeling strategy -- Progressive, Regressive, and Stagnant -- that reduces human cognitive overhead while ensuring high cross-an...
| Authors | Yiming Mao, Zixi Yu, Weixin Mao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 3 April 2026 |
| Last updated at source | 21 April 2026 |
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