1 citations · 1 across the 5 of their papers we have counts for
6 papers
Latent Policy Steering: An Efficient and Flexible Framework for Cross-Embodiment Transfer
Yiqi Wang, Mrinal Verghese, Jeff Schneider
The performance of learned robot visuomotor policies depends heavily on the size and quality of their training data, yet collecting high-quality demonstrations remains costly for r…
Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models
Wen-Tse Chen, Jiayu Chen, Fahim Tajwar +4
Learning from self-sampled data and sparse environmental feedback remains a fundamental challenge in training self-evolving agents. Temporal credit assignment mitigates this issue…
Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions
Aman Mehra, Alexandre Capone, Jeff Schneider
A long-standing problem in online reinforcement learning (RL) is of ensuring sample efficiency, which stems from an inability to explore environments efficiently. Most attempts at…
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…
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning
Jiayu Chen, Le Xu, Aravind Venugopal +1
Offline reinforcement learning (RL) offers a powerful paradigm for data-driven control. Compared to model-free approaches, offline model-based RL (MBRL) explicitly learns a world m…
UGV-UAV Object Geolocation in Unstructured Environments
David Guttendorf, D. W. Wilson Hamilton, Anne Harris Heckman +12
A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) has the potential for advancing autonomous object geolocation performance. Much res…