Learning predictive models from interaction with the world allows an agent, such as a robot,
to learn about how the world works, and then use this learned model to plan coordinated …

ROBEL is an open-source platform of cost-effective robots designed for reinforcement
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …

Training agents to autonomously control anthropomorphic robotic hands has the potential to
lead to systems capable of performing a multitude of complex manipulation tasks in …

In this letter, we focus on inferring whether the given user command is clear, ambiguous, or
infeasible in the context of interactive robotic agents utilizing large language models (LLMs) …

Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …

We present Galactic, a large-scale simulation and reinforcement-learning (RL) framework for
robotic mobile manipulation in indoor environments. Specifically, a Fetch robot (equipped …

Learning from demonstration is a powerful method for teaching robots new skills, and more
demonstration data often improves policy learning. However, the high cost of collecting …

A key challenge for robotic manipulation in open domains is how to acquire diverse and
generalizable skills for robots. Recent progress in one-shot imitation learning and robotic …

In mobile manipulation (MM), robots can both navigate within and interact with their
environment and are thus able to complete many more tasks than robots only capable of …

General-purpose robotic systems must master a large repertoire of diverse skills. While
reinforcement learning provides a powerful framework for acquiring individual behaviors, the …