Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models
This paper proposes Foresight Residual RL, which improves long-horizon robot manipulation success by augmenting each subtask's sparse success reward with an offline-estimated foresight value—the probability of future subtask success conditioned on the terminal state of the current subtask. On a three-phase wrench-based nut-tightening task in Isaac Gym, it achieves 85.6% full-task success, outperforming standard subtask residual RL (54.5%) and VLA baselines.
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[Submitted on 17 Jul 2026]
Title:Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models
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Abstract:Vision-Language-Action (VLA) policies offer strong general-purpose manipulation priors, but often fail on tight-tolerance, contact-rich assembly due to long-horizon credit assignment and subtask coupling: a state that is geometrically successful for the current skill can be brittle for downstream skills. We show this failure mode in residual reinforcement learning (RL) over a frozen VLA base policy: constant sparse success rewards improve each subtask in isolation yet yield little or no gain when skills are chained, because terminal state quality is uncontrolled. We propose Foresight Residual RL, which optimizes handoff quality by augmenting each subtask's sparse success reward with an offline-estimated foresight value -- the probability of future subtask success conditioned on the terminal state of the current subtask. Concretely, we (i) train a visual foresight predictor from images of terminal states of the base policy, labeled using downstream rollout statistics, and (ii) train residual policies via backward foresight induction, using the predictor output as a reward multiplier. On a three-phase wrench-based nut-tightening assembly task in Isaac Gym (grasp, move-insert, rotate), our method achieves 85.6% full-task success, outperforming standard subtask residual RL (54.5%) and VLA baselines, while leaving per-subtask success unchanged. These results highlight that improving long-horizon performance requires shaping which successful states are produced at each sub-task, not only whether success occurs.
Comments: Accepted at IROS2026. Project website: this https URL
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2607.16506 [cs.RO]
(or arXiv:2607.16506v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.16506
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuhan Liu [view email] [v1] Fri, 17 Jul 2026 21:00:28 UTC (5,741 KB)
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