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cs.RO2026
RAFT: Reasoning-Aligned Fine-Tuning Improves Robustness of VLA Post-Training
Shresth Grover, Andrey Kolobov, Dhruv Shah
Adapting a generalist vision-language-action (VLA) policy to a target task with limited demonstrations can improve nominal task success while reducing robustness to variations in a…
cs.RO2026
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
Lihan Zha, Shresth Grover, Tenny Yin +7
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for c…
cs.RO2025
Enhancing Generalization in Vision-Language-Action Models by Preserving Pretrained Representations
Shresth Grover, Akshay Gopalkrishnan, Bo Ai +3
Vision-language-action (VLA) models finetuned from vision-language models (VLMs) hold the promise of leveraging rich pretrained representations to build generalist robots across di…