6 accepted papers
Recent Imitation Learning (IL) techniques focus on adversarial imitation learning algorithms to learn from a fixed set of expert demonstrations. While these approaches are theoretically sound, they suffer from a number of problems such as poor sample efficiency, poor stability, and a host of issues…
This paper outlines an algorithmic approach for the automatic coordination and planning of heterogeneous multi-robot teams. Specifically, this work addresses the marsupial-based subset of multi-robot teams, where “carrier” robots transport and deploy “passenger” robots. The approach starts with a hi…
In this paper, we investigate the use of surrogate agents to accelerate test scenario generation for autonomous vehicles. Our goal is to train the surrogate to replicate the true performance modes of the system. We create these surrogates by utilizing imitation learning with deep neural networks. By…
To properly evaluate the ability of robots to operate autonomously in the real world, it is necessary to develop methods for quantifying their self-righting capabilities. Here, we improve upon a sampling-based framework for evaluating self-righting capabilities that was previously validated in two d
We propose a novel method for generating test scenarios for a black box autonomous system that demonstrate critical transitions in its performance modes. In complex environments it is possible for an autonomous system to fail at its assigned mission even if it complies with requirements for all subs…
In this paper, we consider the problem of guarding a valuable naval asset from a highly maneuverable threat via the use of autonomous unmanned surface vehicles (USVs) as dynamic obstacles. The objective of the defending agent is to maximize the amount of time it takes an intruder boat to enter the r…