Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Large language models can encode a wealth of semantic knowledge about the world. Such
knowledge could be extremely useful to robots aiming to act upon high-level, temporally …

Recent progress in large language models (LLMs) has demonstrated the ability to learn and
leverage Internet-scale knowledge through pre-training with autoregressive models …

In this paper, we study the problem of enabling a vision-based robotic manipulation system
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …

This paper proposes an approach to automate chemistry experiments using robots by
translating natural language instructions into robot-executable plans, using large language …

We present OAKINK2 a dataset of bimanual object manipulation tasks for complex daily
activities. In pursuit of constructing the complex tasks into a structured representation …

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

Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …

We introduce an approach for augmenting model-free deep reinforcement learning agents
with a mechanism for relational reasoning over structured representations, which improves …