Meenal Parakh will present her General Exam "AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation" on Tuesday, May 6, 2025 at 4:30 PM in FC 202. Committee Members: Jia Deng (advisor), Szymon Rusinkiewicz, Adam Finkelstein 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, sharing the same link structure), extrapolation (testing on a robot with a different link structure), and composition (testing on combinations of link structure). We primarily test RL agents trained under multitask objectives and evaluated for their multi-task performance and zero-shot transfer. Our study aims to answer whether morphology-randomized training can outperform single-embodiment baselines, whether zero-shot generalization to unseen morphologies is feasible, and how consistently these patterns hold across different generalization regimes. The results highlight the current limitations of multi-embodiment learning and provide insights into how architectural and training design choices influence policy generalization. Reading List: https://docs.google.com/document/d/1Ey-U4ydkMTrTO6Qdr9FmycVom5daALctNbeyRjwQ... Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.