activity
20242026
most citedMOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Artifacts as Memory Beyond the Agent Boundary

John D. Martin, Fraser Mince, Esra'a Saleh +1

The situated view of cognition holds that intelligent behavior depends not only on internal memory, but on an agent's active use of environmental resources. Here, we begin formaliz…

cs.HC2025

Cybernetic Marionette: Channeling Collective Agency Through a Wearable Robot in a Live Dancer-Robot Duet

Anup Sathya, Jiasheng Li, Zeyu Yan +4

We describe DANCE^2, an interactive dance performance in which audience members channel their collective agency into a dancer-robot duet by voting on the behavior of a wearable rob…

cs.LG2025

On the Interplay Between Sparsity and Training in Deep Reinforcement Learning

Fatima Davelouis, John D. Martin, Michael Bowling

We study the benefits of different sparse architectures for deep reinforcement learning. In particular, we focus on image-based domains where spatially-biased and fully-connected a…

cs.LG2024

Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning

Bradley Burega, John D. Martin, Luke Kapeluck +1

We study how a Reinforcement Learning (RL) system can remain sample-efficient when learning from an imperfect model of the environment. This is particularly challenging when the le…

cs.LG2024★ 1 cited

MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning

Rafael Rafailov, Kyle Hatch, Victor Kolev +3

We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent of…