Our goal is for robots to follow natural language instructions like "put...
We present a novel observation about the behavior of offline reinforceme...
We propose Heuristic Blending (HUBL), a simple performance-improving
tec...
A rich representation is key to general robotic manipulation, but existi...
Only a small percentage of blind and low-vision people use traditional
m...
Simulated humanoids are an appealing research domain due to their physic...
A broad challenge of research on generalization for sequential
decision-...
We provide a framework for accelerating reinforcement learning (RL)
algo...
A highly desirable property of a reinforcement learning (RL) agent – and...
The NeurIPS 2020 Procgen Competition was designed as a centralized bench...
Despite its promise, reinforcement learning's real-world adoption has be...
Existing multi-armed bandit (MAB) models make two implicit assumptions: ...
Small uninhabited aerial vehicles (sUAVs) commonly rely on active propul...
Autonomous soaring capability has the potential to significantly increas...
The conventional model for online planning under uncertainty assumes tha...
Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in...