AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation
Meenal Parakh, Alexandre Kirchmeyer, Beining Han, Jia Deng

TL;DR
This paper introduces a standardized benchmark suite for evaluating the ability of reinforcement learning policies to generalize manipulation skills across different robot morphologies, addressing a key challenge in scalable robotics learning.
Contribution
It provides a systematic benchmark for cross-embodiment manipulation tasks, enabling consistent evaluation of policy generalization across diverse robot morphologies.
Findings
Morphology-aware training can outperform single-embodiment baselines.
Zero-shot generalization to unseen morphologies is challenging but possible.
Architectural and training choices significantly impact generalization performance.
Abstract
Generalizing control policies to novel embodiments remains a fundamental challenge in enabling scalable and transferable learning in robotics. While prior works have explored this in locomotion, a systematic study in the context of manipulation tasks remains limited, partly due to the lack of standardized benchmarks. In this paper, we introduce a benchmark for learning cross-embodiment manipulation, focusing on two foundational tasks-reach and push-across a diverse range of morphologies. The benchmark is designed to test generalization along three axes: interpolation (testing performance within a robot category that shares the same link structure), extrapolation (testing on a robot with a different link structure), and composition (testing on combinations of link structures). On the benchmark, we evaluate the ability of different RL policies to learn from multiple morphologies and to…
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Taxonomy
TopicsHuman Pose and Action Recognition
