8 citations · 9 across the 7 of their papers we have counts for
4 papers · 1 filter
An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning
Mino Nakura, Sriram Krishna, Yufei Wang +3
Visual imitation learning is a promising approach to training robot manipulation policies capable of completing a wide variety of tasks. However, policies today remain brittle to v…
H2RBench: A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer
Chuyang Xiao, Haotian Zhan, Sriram Krishna +4
Learning robot manipulation policies from human video demonstrations constitutes a promising avenue for scalable robot learning. However, comparing different human-to-robot (H2R) t…
From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation
Ying Yuan, Xinyu Liu, Sriram Krishna +1
Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pick-and-place manipulation. W…
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
Sriram Krishna, Ben Eisner, Haotian Zhan +5
We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy…