Large-scale task planning is a major challenge. Recent work exploits large language
models (LLMs) directly as a policy and shows surprisingly interesting results. This paper …

To standardize definitions and guide the design, regulation, and policy related to automated
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …

We present a relational graph learning approach for robotic crowd navigation using model-
based deep reinforcement learning that plans actions by looking into the future. Our …

This article considers the problem of diagnosing certain common errors in reward design. Its
insights are also applicable to the design of cost functions and performance metrics more …

This paper presents INVIGORATE, a robot system that interacts with human through natural
language and grasps a specified object in clutter. The objects may occlude, obstruct, or even …

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially,
in the presence of many aggressive, high-speed traffic participants. This paper presents …

Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a
very challenging task. This is because human movement and interactions are very hard to …

In order to navigate safely and effectively with humans in close proximity, robots must be
capable of predicting the future motions of humans. This study first consolidates human …

Driving through dense urban environments is difficult for autonomous vehicles because they
must reason about the unknown intentions of a large number of road users while also …

Motion planning and decision-making while considering uncertainty is critical for an
autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new …