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 …

Machine learning has long since become a keystone technology, accelerating science and
applications in a broad range of domains. Consequently, the notion of applying learning …

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 …

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 …

Long-horizon tasks, usually characterized by complex subtask dependencies, present a
significant challenge in manipulation planning. Skill chaining is a practical approach to …

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

Robots need robust models to effectively perform tasks that humans do on a daily basis.
These models often require substantial developmental costs to maintain because they need …

We consider the problem of sequential manipulationand tool-use planning in domains that
include physical interac-tions such as hitting and throwing. The approach integrates aTask …