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This paper presents TIGER, a novel framework that enhances reinforcement learning efficiency for sparse-reward robotic manipulation by integrating imitation learning as a task-space progress estimator. By converting predicted action chunks into end-effector references, the method provides dense intermediate rewards and reduces off-manifold exploration during early training phases. The approach demonstrates improved sample efficiency and safety on both simulated and real tabletop robots without compromising final success rates.
Task-Space Imitation Guidance for Efficient Reinforcement Learning
arXiv:2610.07527v1 Announce Type: new Abstract: We introduce Task-Space Imitation Guidance for Efficient Reinforcement Learning (TIGER), a reward-construction and pretraining framework for sparse-reward tabletop robotic manipulation. TIGER treats an action-chunked imitation policy not as an executable controller or action prior, but as a local task-space progress estimator: predicted action chunks are converted, using controller-aware action-to-motion mapping, into short-horizon end-effector references, and the RL agent receives dense progress rewards toward these references while the sparse environment reward remains the dominant objective. During pretraining, TIGER uses imitation-guided look-ahead signals to relax conservative value penalties for actions predicted to make task-space progress, reducing off-manifold exploration during early online RL. Across simulation and real-robot experiments, TIGER improves early sample efficiency and reduces measured safety violations while matching or improving final success rates relative to prior RL and IL-RL baselines on the evaluated tasks.
Abstract:We introduce Task-Space Imitation Guidance for Efficient Reinforcement Learning (TIGER), a reward-construction and pretraining framework for sparse-reward tabletop robotic manipulation. TIGER treats an action-chunked imitation policy not as an executable controller or action prior, but as a local task-space progress estimator: predicted action chunks are converted, using controller-aware action-to-motion mapping, into short-horizon end-effector references, and the RL agent receives dense progress rewards toward these references while the sparse environment reward remains the dominant objective. During pretraining, TIGER uses imitation-guided look-ahead signals to relax conservative value penalties for actions predicted to make task-space progress, reducing off-manifold exploration during early online RL. Across simulation and real-robot experiments, TIGER improves early sample efficiency and reduces measured safety violations while matching or improving final success rates relative to prior RL and IL-RL baselines on the evaluated tasks.
Robotics (cs.RO)
arXiv:2610.07527 [cs.RO]
(or arXiv:2610.07527v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.07527 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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