The problem of planning for a robot that operates in environments containing a large
number of objects, taking actions to move itself through the world as well as to change the …

Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …

Abstract We propose Text2Motion, a language-based planning framework enabling robots
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …

Recent works have shown how the reasoning capabilities of Large Language Models
(LLMs) can be applied to domains beyond natural language processing, such as planning …

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 …

Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in
purely abstract terms by imagining action sequences and scoring their likelihood of success …

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

Exploration in environments with sparse rewards has been a persistent problem in
reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and …

We describe a framework for research and evaluation in Embodied AI. Our proposal is
based on a canonical task: Rearrangement. A standard task can focus the development of …

Representing robotic manipulation tasks as constraints that associate the robot and the
environment is a promising way to encode desired robot behaviors. However, it remains …