[Submitted on 28 Sep 2026]
Title:SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets
View a PDF of the paper titled SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets, by Yuto Tanaka and 4 other authors
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Abstract:Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when datasets are contaminated with suboptimal or noisy demonstrations. While prior quality-assessment methods attempt to filter or reweight data, they typically rely on manual pre-selection of expert reference data or task-specific heuristics, limiting scalability. To address this challenge, we introduce SynIL (Synergy-based Imitation Learning), a novel framework for automated, label-free demonstration quality assessment in offline reinforcement learning. Grounded in neuroscientific evidence that motor synergy, a low-dimensional coordinated structure in movement, correlates directly with motor proficiency, SynIL algorithmically quantifies synergy manifestation to generate dense, transition-level reward signals via self-supervised reward regression. Comprehensive evaluations on D4RL locomotion benchmarks and multi-human Robomimic manipulation datasets demonstrate that synergy-derived rewards correlate strongly with ground-truth rewards. Furthermore, SynIL substantially outperforms Behavior Cloning (BC) and achieves performance comparable to, and in sparse-reward human teleoperation scenarios, superior to, offline reinforcement learning trained on true environment rewards.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.38225 [cs.RO]
(or arXiv:2609.38225v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.38225
arXiv-issued DOI via DataCite
Submission history
From: Martina Doku [view email] [v1] Mon, 28 Sep 2026 06:04:51 UTC (4,117 KB)
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