Large language models excel at a wide range of complex tasks. However, enabling general
inference in the real world, eg, for robotics problems, raises the challenge of grounding. We …

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

Task and motion planning (TAMP) integrates high-level task planning and low-level motion
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …

We present a method to learn compositional multi-object dynamics models from image
observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and …

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

We present ObSuRF, a method which turns a single image of a scene into a 3D model
represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to …

We present a visually grounded hierarchical planning algorithm for long-horizon
manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning …

Accurate object rearrangement from vision is a crucial problem for a wide variety of real-
world robotics applications in unstructured environments. We propose IFOR, Iterative Flow …

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

Robotic planning problems in hybrid state and action spaces can be solved by integrated
task and motion planners (TAMP) that handle the complex interaction between motion-level …