17,969 research outputs found
Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration
Understanding human intentions is critical for safe and effective human-robot
collaboration. While state of the art methods for human goal prediction utilize
learned models to account for the uncertainty of human motion data, that data
is inherently stochastic and high variance, hindering those models' utility for
interactions requiring coordination, including safety-critical or
close-proximity tasks. Our key insight is that robot teammates can deliberately
configure shared workspaces prior to interaction in order to reduce the
variance in human motion, realizing classifier-agnostic improvements in goal
prediction. In this work, we present an algorithmic approach for a robot to
arrange physical objects and project "virtual obstacles" using augmented
reality in shared human-robot workspaces, optimizing for human legibility over
a given set of tasks. We compare our approach against other workspace
arrangement strategies using two human-subjects studies, one in a virtual 2D
navigation domain and the other in a live tabletop manipulation domain
involving a robotic manipulator arm. We evaluate the accuracy of human motion
prediction models learned from each condition, demonstrating that our workspace
optimization technique with virtual obstacles leads to higher robot prediction
accuracy using less training data.Comment: International Conference on Human-Robot Interactio
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
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