In this work, we propose a novel shared autonomy framework to operate
ar...
Collaborative autonomous multi-agent systems covering a specified area h...
Using a single camera to estimate the distances of objects reduces costs...
The Bayesian paradigm provides a rigorous framework for estimating the w...
In this paper, we develop a neural network model to predict future human...
Human motion prediction is a fundamental part of many human-robot
applic...
Robots and artificial agents that interact with humans should be able to...
Hierarchical multi-agent reinforcement learning (MARL) has shown a
signi...
In this paper, we propose a Visual Teach and Repeat (VTR) algorithm usin...
Multi-agent collision-free trajectory planning and control subject to
di...
Real-time, guaranteed safe trajectory planning is vital for navigation i...
Hamilton-Jacobi reachability analysis is a powerful technique used to ve...
Action anticipation, intent prediction, and proactive behavior are all
d...
Safety is an important topic in autonomous driving since any collision m...
This paper investigates a hybrid solution which combines deep reinforcem...
Model-free reinforcement learning (RL) is capable of learning control
po...
This article describes a dataset collected in a set of experiments that
...
Autonomous coverage of a specified area by robots operating in close
pro...
In Bansal et al. (2019), a novel visual navigation framework that combin...
Autonomous systems operating in close proximity with each other to cover...
Model-free reinforcement learning (RL) provides an attractive approach f...
Action anticipation, intent prediction, and proactive behavior are all
d...
In the pursuit of real-time motion planning, a commonly adopted practice...
Reach-avoid problems involve driving a system to a set of desirable
conf...
Model-free Reinforcement Learning (RL) offers an attractive approach to ...