Data Analogies Enable Efficient Cross-Embodiment Transfer

2026-03-06

AI Summary

This study investigates how to effectively organize heterogeneous robotic demonstration data to enhance cross-embodiment transfer performance. Through controlled simulation experiments, it systematically compares the efficacy of unpaired large-scale data against structured paired data—such as demonstrations aligned by scene, task, or trajectory—under varying morphologies and viewpoints. The findings reveal that, for morphology differences, structured data analogies are more effective than merely increasing data diversity, highlighting distinct data structure requirements for morphology transfer versus viewpoint transfer. By optimizing data composition alone, the approach achieves an average 22.5% improvement in success rate on real-world cross-embodiment transfer tasks, underscoring the critical role of data analogy in enabling effective embodiment-agnostic skill transfer.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Analogy
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Generalist robot policies are trained on demonstrations collected across a wide variety of robots, scenes, and viewpoints. Yet it remains unclear how to best organize and scale such heterogeneous data so that it genuinely improves performance in a given target setting. In this work, we ask: what form of demonstration data is most useful for enabling transfer across robot set-ups? We conduct controlled experiments that vary end-effector morphology, robot platform appearance, and camera perspective, and compare the effects of simply scaling the number of demonstrations against systematically broadening the diversity in different ways. Our simulated experiments show that while perceptual shifts such as viewpoint benefit most from broad diversity, morphology shifts benefit far less from unstructured diversity and instead see the largest gains from data analogies, i.e. paired demonstrations that align scenes, tasks, and/or trajectories across different embodiments. Informed by the simulation results, we improve real-world cross-embodiment transfer success by an average of $22.5\%$ over large-scale, unpaired datasets by changing only the composition of the data.

Problem

• cross-embodiment transfer
• robot policy
• demonstration data
• data diversity
• data analogy

Innovation

• data analogies
• cross-embodiment transfer
• robotic generalist policies
• paired demonstrations
• morphology shift