Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

Advancements in artificial intelligence algorithms and models, along with embedded device
support, have resulted in the issue of high energy consumption and poor compatibility when …

This letter presents a planning system for autonomous driving among many pedestrians. A
key ingredient of our approach is Pedestrian Optimal Reciprocal Collision Avoidance, a …

In this article, we present an efficient planning system for automated vehicles in highly
interactive environments (EPSILON). EPSILON is an efficient interaction-aware planning …

The uncertainty and dynamic of home environment present great challenges to the task
planning of service robots. The nature of the home environment is highly unstructured, with a …

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 …

State-of-the-art sampling-based online POMDP solvers compute near-optimal policies for
POMDPs with very large state spaces. However, when faced with large observation spaces …

Autonomous driving in urban environments requires the capability of merging into narrow
gaps. In cases of high traffic density this becomes more complex since one must consider …

This paper presents a novel online framework for safe crowd-robot interaction based on risk-
sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk …

We focus on the problem of planning the motion of a robot in a dynamic multiagent
environment such as a pedestrian scene. Enabling the robot to navigate safely and in a …