Humans possess a remarkable ability to make decisions within real-world environments that
are expansive, complex, and multidimensional. Human cognitive computational …

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 …

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

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

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 …

Robots will be expected to manipulate a wide variety of objects in complex and arbitrary
ways as they become more widely used in human environments. As such, the …

We present a strategy for designing and building very general robot manipulation systems
using a general-purpose task-and-motion planner with both engineered and learned …

Human environments contain numerous objects configured in a variety of arrangements.
Our goal is to enable robots to repose previously unseen objects according to learned …

While large language models (LLMs) are successful in completing various language
processing tasks, they easily fail to interact with the physical world by generating control …