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Cross-Embodiment Offline Reinforcement Learning for Heterogeneous Robot Datasets

arXiv:2602.18025v1 Announce Type: new Abstract: Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this study, we address this issue by uniting offline reinforcement learning (offline RL) with cross-embodiment learning. Offline RL leverages both expert and abundant suboptimal data, and cross-embodiment learning aggregates heterogeneous robot trajectories across diverse morphologies to acquire universal control priors. We perform a systematic analysis of this offline RL and cross-embodiment paradigm, providing a principled understanding of its strengths and limitations. To evaluate this offline RL and cross-embodiment paradigm, we construct a suite of locomotion datasets spanning 16 distinct robot platforms. Our experiments confirm that this combined approach excels at pre-training with datasets rich in suboptimal trajectories, outperforming pure behavior cloning. However, as the proportion of suboptimal

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Haruki Abe, Takayuki Osa, Yusuke Mukuta, Tatsuya Harada · · 1 min read · 22 views
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arXiv:2602.18025v1 Announce Type: new Abstract: Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this study, we address this issue by uniting offline reinforcement learning (offline RL) with cross-embodiment learning. Offline RL leverages both expert and abundant suboptimal data, and cross-embodiment learning aggregates heterogeneous robot trajectories across diverse morphologies to acquire universal control priors. We perform a systematic analysis of this offline RL and cross-embodiment paradigm, providing a principled understanding of its strengths and limitations. To evaluate this offline RL and cross-embodiment paradigm, we construct a suite of locomotion datasets spanning 16 distinct robot platforms. Our experiments confirm that this combined approach excels at pre-training with datasets rich in suboptimal trajectories, outperforming pure behavior cloning. However, as the proportion of suboptimal data and the number of robot types increase, we observe that conflicting gradients across morphologies begin to impede learning. To mitigate this, we introduce an embodiment-based grouping strategy in which robots are clustered by morphological similarity and the model is updated with a group gradient. This simple, static grouping substantially reduces inter-robot conflicts and outperforms existing conflict-resolution methods.

Executive Summary

This study introduces a novel approach to scalable robot policy pre-training by combining offline reinforcement learning (RL) with cross-embodiment learning. The authors evaluate this paradigm using a suite of locomotion datasets across 16 distinct robot platforms, demonstrating its effectiveness in pre-training with suboptimal trajectories. However, they also identify limitations, including conflicting gradients across morphologies, and propose an embodiment-based grouping strategy to mitigate this issue. The results show that this approach outperforms existing methods and pure behavior cloning, highlighting its potential for efficient robot policy pre-training.

Key Points

  • ▸ Combination of offline RL and cross-embodiment learning for robot policy pre-training
  • ▸ Evaluation using a suite of locomotion datasets across 16 distinct robot platforms
  • ▸ Introduction of an embodiment-based grouping strategy to mitigate conflicting gradients

Merits

Improved Efficiency

The proposed approach enables efficient robot policy pre-training by leveraging suboptimal trajectories and aggregating heterogeneous robot data

Demerits

Conflicting Gradients

The study identifies conflicting gradients across morphologies as a limitation, which can impede learning as the proportion of suboptimal data and number of robot types increase

Expert Commentary

The study's combination of offline RL and cross-embodiment learning represents a significant advancement in robot policy pre-training. The introduction of an embodiment-based grouping strategy to mitigate conflicting gradients is a notable contribution, as it enables more efficient learning across heterogeneous robot platforms. However, further research is needed to fully explore the potential of this approach and address remaining limitations. The study's findings have important implications for the development of more efficient and scalable robotics systems, and highlight the need for continued innovation in this field.

Recommendations

  • ✓ Further research on the application of offline RL and cross-embodiment learning to various robotics tasks
  • ✓ Development of more advanced conflict-resolution methods to improve the efficiency and scalability of robot policy pre-training

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