Author

Sriram Krishna

Carnegie Mellon University

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Preprint Sep 2026

H2RBench: A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer

Learning robot manipulation policies from human video demonstrations constitutes a promising avenue for scalable robot learning. However, comparing different human-to-robot (H2R) transfer methods remains challenging, as existing approaches are evaluated under different settings, including differing task suites, scene l...

Chu-Yang Xiao, Hao-Tian Zhan, Sriram Krishna et al. · 0 citations

#machine learning Preprint Sep 2026

An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning

A controlled empirical study of which design choices allow visuomotor policies to generalize across viewpoints finds that viewpoint generalization improves when dense visual tokens are retained and the action head participates in geometric reasoning.

Mino Nakura, Sriram Krishna, Yu-Fei Wang et al. · 1 citation

Jun 2026

From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation

This work adapts a hierarchical imitation learning framework that combines high-level hand sub-goal prediction with a low-level goal-conditioned controller to serve not only as resources for grasp synthesis, but also as scalable pretraining data for contact-rich dexterous manipulation.

Ying Yuan, Xinyu Liu, Sriram Krishna et al. · arXiv.org · 0 citations