The content delves into the concept of heterogeneous cross-embodiment learning, where a single policy is trained to control various robots in different environments. By combining navigation and manipulation data, the study aims to enhance generalization and transferability across diverse robotic tasks. The results show significant improvements in success rates for both manipulation and navigation tasks when co-training with diverse datasets. The analysis highlights the potential of leveraging data diversity to create more versatile robotic policies capable of controlling a wide range of embodiments.
The study investigates how including navigation data can benefit manipulators by enhancing spatial understanding and goal-reaching capabilities. It also examines how manipulation data can improve navigation policies by providing richer object-centric interactions. Through empirical evaluations on various tasks and robots, the research demonstrates the effectiveness of cross-embodiment learning in improving performance across different robotic domains.
Key metrics such as success rates, correlations between features, and comparisons between different dataset mixtures are used to evaluate the impact of cross-embodiment learning on robotic policies. The findings suggest that integrating diverse datasets from navigation and manipulation domains can lead to more robust and adaptable robot control strategies.
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by Jonathan Yan... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.19432.pdfDeeper Inquiries