5 papers
LEMCA: LLM-Guided Synthesis of Efficient Mode-Switching Control Architectures
Arjun Krishna, Vincent Pacelli, Dinesh Jayaraman
Physical control tasks in the natural world, such as driving or object manipulation, frequently exhibit dramatic variations in sensory and compute complexity over time. Correspondi…
Let it Cook: Learning to Wait in Sequential Decision Making
Christopher Watson, Arjun Krishna, Dinesh Jayaraman +1
In sequential decision making, an agent typically observes its environment and acts at every timestep. However, such active participation may not always be necessary; tasks such as…
Real-World Reinforcement Learning of Active Perception Behaviors
Edward S. Hu, Jie Wang, Xingfang Yuan +5
A robot's instantaneous sensory observations do not always reveal task-relevant state information. Under such partial observability, optimal behavior typically involves explicitly…
The Belief State Transformer
Edward S. Hu, Kwangjun Ahn, Qinghua Liu +9
We introduce the "Belief State Transformer", a next-token predictor that takes both a prefix and suffix as inputs, with a novel objective of predicting both the next token for the…
Privileged Sensing Scaffolds Reinforcement Learning
Edward S. Hu, James Springer, Oleh Rybkin +1
We need to look at our shoelaces as we first learn to tie them but having mastered this skill, can do it from touch alone. We call this phenomenon "sensory scaffolding": observatio…