Autonomous mobility tasks such as lastmile delivery require reasoning ab...
The 2nd BARN (Benchmark Autonomous Robot Navigation) Challenge took plac...
Given a dataset of expert agent interactions with an environment of inte...
While combining imitation learning (IL) and reinforcement learning (RL) ...
While current systems for autonomous robot navigation can produce safe a...
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at...
Accurate control of robots in the real world requires a control system t...
One of the key challenges in high speed off road navigation on ground
ve...
Social navigation is the capability of an autonomous agent, such as a ro...
The imitation learning research community has recently made significant
...
Autonomous mobile robots deployed in outdoor environments must reason ab...
Classical autonomous navigation systems can control robots in a
collisio...
A longstanding goal of artificial intelligence is to create artificial a...
While imitation learning for vision based autonomous mobile robot naviga...
While current autonomous navigation systems allow robots to successfully...
While Adversarial Imitation Learning (AIL) algorithms have recently led ...
Learning from demonstrations in the wild (e.g. YouTube videos) is a
tant...
In imitation learning from observation IfO, a learning agent seeks to im...
Moving in complex environments is an essential capability of intelligent...
While classical autonomous navigation systems can typically move robots ...
Classical navigation systems typically operate using a fixed set of
hand...
Experience replay (ER) improves the data efficiency of off-policy
reinfo...
The sim to real transfer problem deals with leveraging large amounts of
...
Robot control policies learned in simulation do not often transfer well ...
Robots can learn to do complex tasks in simulation, but often, learned
b...
While classical approaches to autonomous robot navigation currently enab...
Existing autonomous robot navigation systems allow robots to move from o...
While deep reinforcement learning techniques have led to agents that are...
Imitation from observation is the framework of learning tasks by observi...
Imitation learning has long been an approach to alleviate the tractabili...
Imitation learning is the process by which one agent tries to learn how ...
Classically, imitation learning algorithms have been developed for ideal...
While deep reinforcement learning techniques have led to agents that are...
While deep reinforcement learning (DRL) has led to numerous successes in...
Imitation from observation (IfO) is the problem of learning directly fro...
Humans often learn how to perform tasks via imitation: they observe othe...
While recent advances in deep reinforcement learning have allowed autono...
We consider discriminative dictionary learning in a distributed online
s...
We provide two novel adaptive-rate compressive sensing (CS) strategies f...