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

Yuto Tanaka, Kyo Kutsuzawa, Martina Doku, Dai Owaki, Mitsuhiro Hayashibe

arXiv:2609.38225v1cs.ROcs.AI

TL;DR

Low-quality demonstrations can undermine imitation learning, and prior quality-assessment approaches require manual identification of expert data. SynIL assesses demonstrations through motor synergy and uses the scores to derive rewards for offline reinforcement learning. It outperformed Behavior Cloning on most datasets and was comparable to offline reinforcement learning using true rewards.

  • Problem

    Suboptimal or noisy demonstrations can reduce imitation-learning performance, while prior quality-assessment approaches require humans to pre-identify expert data.

    Source: §1 · p. 2

  • Method

    SynIL algorithmically assesses demonstration quality using synergy and uses the resulting scores as rewards for offline reinforcement learning.

    Source: §1 · p. 3, §3.1 · p. 5

  • Results

    SynIL outperformed BC on most datasets and was comparable to IQL with true rewards, with strong performance across benchmark locomotion and human teleoperation datasets.

    Source: §4.4.2 · p. 9, §6 · p. 11

  • Takeaways & Limitations

    The findings support synergy as a surrogate reward signal across benchmark locomotion and human teleoperation datasets.

    Source: §6 · p. 11

    Synergy is not universally equivalent to optimality, and the study could not establish that their relationship generalizes.

    Source: §5.3 · p. 11

Abstract

from arXiv · show

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.

1. Introduction

Low-quality demonstrations can reduce imitation-learning performance, while prior quality-assessment approaches require humans to identify expert data. SynIL uses motor synergy to assess demonstrations and derive rewards for offline reinforcement learning.

  • Suboptimal or noisy demonstrations can cause imitation learning to reproduce poor behavior and decrease performance.Source: §1 · p. 2
  • Prior quality-assessment methods rely on humans to pre-determine expert data, motivating automatic quantitative assessment.Source: §1 · p. 3
  • Motor synergy is a low-dimensional structure of coordinated muscles and joints associated with movement proficiency and can be quantified algorithmically.Source: §1 · p. 3
  • SynIL proposes algorithmic synergy-based demonstration assessment and uses the resulting quality scores as rewards for learning.Source: §1 · p. 3
  • The study presents the first application of synergy to assess demonstration-movement quality in imitation learning.Source: §1 · p. 4
  • SynIL improved imitation-learning performance across various datasets, including real human demonstrations.Source: §1 · p. 4

2. Related Work

This section outlines imitation learning and offline reinforcement learning, then reviews methods for handling demonstration quality. Existing quality-aware approaches may require expert-data preselection or discard useful state-action information, motivating automatic, transition-level assessment.

2.1. Imitation Learning

  • BC learns to mimic expert state-action pairs offline, but equal sample weighting makes it sensitive to demonstration quality and can compound errors in closed-loop interaction.For continuous actions, deterministic BC often uses a mean-squared-error objective.Source: §2.1 · p. 4

2.2. Offline Reinforcement Learning

  • Online reinforcement learning learns through environment interaction, which can require repeated data collection and pose risks when interacting with robots or vehicles.The section notes that real-world applications remain few despite research in simulation.Source: §2.2 · p. 4
  • Offline reinforcement learning learns from a static dataset without environment interaction, but distribution shift can cause value overestimation for actions unsupported by that dataset.This setting is useful when data collection is costly or dangerous; BCQ and IQL are cited as methods addressing distribution shift.Source: §2.2 · p. 4
  • IQL weights each state-action pair by its advantage, giving SynIL a way to reflect continuous estimates of demonstration quality in policy extraction.Its action-value and value functions are trained from rewards.Source: §2.2 · p. 4

2.3. Algorithms for Demonstration Quality

  • Automatic quality-aware imitation methods either require humans to preselect expert data or remove trajectories, potentially discarding useful transitions and good segments within poor trajectories.The section motivates scoring state-action pairs without removing samples.Source: §2.3 · p. 5

3. Methodology

SynIL derives demonstration-quality rewards from movement synergy and uses them for offline reinforcement learning. It estimates local synergy from state-action trajectories, adjusts the score to avoid overvaluing inactivity, and trains a reward model to approximate trajectory returns.

3.1. Overview

  • SynIL computes synergy scores from demonstrations, converts them into rewards with self-supervised reward regression, then performs offline reinforcement learning without reward, success, or proficiency labels.The method requires no manual demonstration evaluation apart from setting several hyperparameters.Source: §3.1 · p. 5

3.2. Acquisition of Synergy Scores based on Synergy Manifestation

  • Spatial synergy measures low-dimensional coordination in state-action trajectories; PCA evaluates how compactly each local movement segment is represented by synergies.The decomposition uses normalized state and action dimensions, and smaller residuals indicate more salient spatial synergies.Source: §3.2 · pp. 5–6
  • The synergy level is made independent of the number of synergies, while the final score also accounts for state-action standard deviation to avoid highly rating demonstrations with overly simplistic, motionless behavior.A hyperparameter weights the demonstration-action standard deviation.Source: §3.2 · p. 6

3.3. Reward Estimation Based on Synergy

  • SynIL treats each H-step synergy score as an approximation of that segment’s total reward and trains a state-action reward model to satisfy this relation.The reward model uses self-supervised reward regression, replacing the noise level with the synergy score.Source: §3.3 · p. 6
  • The algorithm computes synergy scores from H-step trajectories, fits the reward model by minimizing the SSRR loss, and trains a policy with offline reinforcement learning using the learned rewards.Source: §3.3 · p. 7

4. Evaluation

SynIL is evaluated on datasets with varying demonstration quality, with dataset rewards withheld to test reward estimation from demonstrations. Its synergy-derived rewards correlate with ground-truth rewards, and SynIL outperforms BC on most D4RL datasets and both BC and IQL on Robomimic.

4.1. Dataset

  • D4RL evaluation used Hopper, HalfCheetah, and Walker2d locomotion datasets spanning medium, medium-replay, and medium-expert quality levels.Robomimic evaluation used Can and Square multi-human datasets with 300 teleoperated demonstrations from six operators.Source: §4.1 · p. 7

4.2. Problem Setting

  • The evaluation treated each demonstration as state-action-next-state tuples without rewards or per-tuple quality values, withholding D4RL rewards to emulate demonstration-only settings.The study compared SynIL with BC and with IQL trained using true rewards.Source: §4.2 · p. 7

4.3. Hyperparameters

  • The reported implementation used hand-tuned hyperparameters for SSRR and imitation learning, including η selected to maximize correlation with true rewards.The input to RewardNet was the combined state and action dimensions, and its output was one-dimensional.Source: §4.3 · pp. 7–8, Table 1 · p. 8, Table 2 · p. 8

4.4.1. Correlation between Estimated Reward and Ground-Truth Reward

  • Estimated-reward correlations exceeded 0.8 on all D4RL datasets except Walker2d-medium-v2 and Hopper-medium-replay-v2, and exceeded 0.86 on every medium-expert dataset.Table 3 reports the correlation coefficients between synergy-based and true rewards.Source: §4.4.1 · p. 8, Table 3 · p. 9

4.4.2. Performance Comparison

  • SynIL outperformed BC on most D4RL datasets and was comparable to IQL trained with true rewards, especially on medium-replay and medium-expert data.BC performance decreased on diverse-quality medium-expert data.Source: Table 4 · p. 9, §4.4.2 · p. 9
  • SynIL significantly outperformed both BC and IQL on Robomimic, where performance was measured by success rate across 150 trials.The datasets contained no reward data, so evaluation used sparse rewards for successful episodes.Source: Table 5 · p. 9, §4.4.2 · pp. 9–10

5. Discussion

SynIL’s synergy-derived rewards support strong offline imitation learning in the evaluated datasets, with gains linked to selective use of mixed-quality demonstrations. The discussion presents possible links between synergy and motor learning while noting that synergy is not universally equivalent to optimality and reward-informed hyperparameter tuning remains.

5.1. Effectiveness of Synergy Rewards in Imitation Learning

  • Synergy-based rewards supported high imitation-learning performance and correlated highly with ground-truth rewards in the evaluated datasets.The authors frame these results as evidence that synergy can assess demonstration quality and provide surrogate rewards.Source: §5.1 · p. 10
  • SynIL’s improvement over BC is attributed to selective use of mixed-quality data, with high-quality segments contributing within mediocre trajectories.The discussion identifies Hopper and Walker2d medium-replay and all medium-expert datasets as settings with behavior from substantially different learning stages; Hopper medium is an exception where the proxy can be unhelpful.Source: §5.1 · p. 10
  • Synergy-based rewards outperformed IQL with ground-truth rewards in some D4RL cases and Robomimic, where the true rewards were sparse.The authors caution that this does not establish synergy as a more correct objective; offline RL performance is sensitive to reward scale, terminal handling, implementation, and hyperparameters.Source: §5.1 · p. 10

5.2. Relationship Between Motor Learning and Synergy

  • The authors hypothesize that reinforcement-learning demonstrations become more regular as learning progresses, increasing synergy manifestation and potentially favoring proficient behaviors.They offer related possible explanations for Robomimic and discuss a computational-neuroscience account in which reduced uncertainty may accompany increased coordination.Source: §5.2 · pp. 10–11

5.3. Limitations

  • Synergy is not universally equivalent to optimality; the authors could not establish that relationship’s generality.They consider SynIL most plausible when inefficient demonstrations have unstable corrections and proficient ones use repeatable patterns.Source: §5.3 · p. 11
  • Hyperparameter selection is not yet fully reward-free because η was manually tuned to obtain high correlation with true rewards.The study also notes that the same values were used across diverse D4RL tasks and agent morphologies, suggesting limited task sensitivity.Source: §5.3 · p. 11

6. Conclusion

The conclusion presents SynIL as an offline reinforcement-learning framework that uses synergy-derived quality evaluation and dense rewards from unannotated trajectory segments. It reports performance above Behavior Cloning across benchmark locomotion and human teleoperation datasets, while identifying reward-informed tuning and the conditions linking synergy to task optimality as future-work concerns.

  • SynIL uses algorithmically extracted motor synergy to score unannotated trajectory segments and generate dense rewards through self-supervised regression.The conclusion describes synergy as a surrogate reward for offline reinforcement learning.Source: §6 · p. 11
  • SynIL outperformed Behavior Cloning and showed strong performance across benchmark locomotion and human teleoperation datasets.The conclusion identifies synergy as a useful surrogate reward signal in these evaluations.Source: §6 · p. 11
  • Future work targets reward-free hyperparameter selection, clarifying when synergy aligns with task optimality, and deployment on real-world physical AI platforms.Source: §6 · p. 11

CRediT authorship contribution statement

The contribution statement credits the authors with conceptualization, methodology, and drafting; Tanaka and Kutsuzawa are additionally credited with software and data analysis and interpretation.

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