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SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets

SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets

Yuto Tanaka, Kyo Kutsuzawa, Martina Doku, Dai Owaki, Mitsuhiro Hayashibe · N/A · 2026

manipulation locomotion reinforcement-learning learning-from-demonstration benchmark
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Summary

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.

Abstract Summary

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.

Key Points

  • 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.

Methodology

This section describes the proposed method, SynIL, which derives its name from synergy-based imitation learning. This method allows a policy to imitate demonstrations by performing offline reinforcement learning based on rewards computed from the degree of synergy manifestation.

In SynIL, demonstrations do not have to include rewards; instead, rewards are computed from demonstrations using synergy, a concept proposed in neuroscience that quantifies the degree of movement coordination.

Methodology — SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets
Figure 2: Comparison of synergy-based/true rewards for different datasets in D4RL. The vertical axes correspond to the cumulative rewards over 200 steps estimated by SSRR, and the horizontal axes correspond to the true cumulative rewards over 200 steps.

Evaluation

To evaluate the proposed method, we used D4RL ( Fu et al., 2020 ) and Robomimic ( Mandlekar et al., 2021 ) datasets as demonstration datasets. D4RL is a dataset for imitation learning and offline reinforcement learning, offering datasets for various environments and problem settings. Among various tasks, we specifically used the locomotion dataset as a typical motor task.

The locomotion dataset includes three types of robots: Hopper, HalfCheetah, and Walker2d.

Evaluation — SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets
Figure 3: Learning curves of all environments.

Sources and Demonstrations

Figures are reproduced from Tanaka et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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