Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications.
Using systems of multiple UAVs is the next obvious step in the process of applying this …

In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …

In shared autonomy, a user and autonomous system work together to achieve shared goals.
To collaborate effectively, the autonomous system must know the user's goal. As such, most …

The partially observable Markov decision process (POMDP) provides a principled general
framework for planning under uncertainty, but solving POMDPs optimally is computationally …

POMDPs provide a principled framework for planning under uncertainty, but are
computationally intractable, due to the “curse of dimensionality” and the “curse of history” …

To solve multi-step manipulation tasks in the real world, an autonomous robot must take
actions to observe its environment and react to unexpected observations. This may require …

In shared autonomy, user input and robot autonomy are combined to control a robot to
achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve …

Action anticipation, intent prediction, and proactive behavior are all desirable characteristics
for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring …

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling
sequential decision-making scenarios with probabilistic dynamics. They are the framework …

We address the problem of computing a policy for fully observable non-deterministic (FOND)
planning problems. By focusing on the relevant aspects of the state of the world, we …