Donald Hejna

2 accepted papers

2026

MotIF: Motion Instruction Fine-Tuning

Minyoung Hwang, Donald Hejna, Dorsa Sadigh, Yonatan Bisk

ICRA 2026poster

While success in many robotics tasks can be determined by only observing the final state and how it differs from the initial state - e.g., if an apple is picked up - many tasks require observing the full motion of the robot to correctly determine success. For example, brushing hair requires repeated…

Cited by 0 Source ↗PDF ↗Scholar ↗

2020

Hierarchically Decoupled Imitation For Morphological Transfer

Donald Hejna, Lerrel Pinto, Pieter Abbeel

ICML 2020poster

Learning long-range behaviors on complex high-dimensional agents is a fundamental problem in robot learning. For such tasks, we argue that transferring learned information from a morphologically simpler agent can massively improve the sample efficiency of a more complex one. To this end, we propose…

Cited by 50 Source ↗PDF ↗code ↗Scholar ↗