4 papers
Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Yiqi Wang, Mrinal Verghese, Jeff Schneider
The performance of learned robot visuomotor policies is heavily dependent on the size and quality of the training dataset. Although large-scale robot and human datasets are increas…
Human Preference Modeling Using Visual Motion Prediction Improves Robot Skill Learning from Egocentric Human Video
Mrinal Verghese, Christopher G. Atkeson
We present an approach to robot learning from egocentric human videos by modeling human preferences in a reward function and optimizing robot behavior to maximize this reward. Prio…
Skills Made to Order: Efficient Acquisition of Robot Cooking Skills Guided by Multiple Forms of Internet Data
Mrinal Verghese, Christopher Atkeson
This study explores the utility of various internet data sources to select among a set of template robot behaviors to perform skills. Learning contact-rich skills involving tool us…
User-in-the-loop Evaluation of Multimodal LLMs for Activity Assistance
Mrinal Verghese, Brian Chen, Hamid Eghbalzadeh +2
Our research investigates the capability of modern multimodal reasoning models, powered by Large Language Models (LLMs), to facilitate vision-powered assistants for multi-step dail…