5,817 research outputs found
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
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
Robots that navigate among pedestrians use collision avoidance algorithms to
enable safe and efficient operation. Recent works present deep reinforcement
learning as a framework to model the complex interactions and cooperation.
However, they are implemented using key assumptions about other agents'
behavior that deviate from reality as the number of agents in the environment
increases. This work extends our previous approach to develop an algorithm that
learns collision avoidance among a variety of types of dynamic agents without
assuming they follow any particular behavior rules. This work also introduces a
strategy using LSTM that enables the algorithm to use observations of an
arbitrary number of other agents, instead of previous methods that have a fixed
observation size. The proposed algorithm outperforms our previous approach in
simulation as the number of agents increases, and the algorithm is demonstrated
on a fully autonomous robotic vehicle traveling at human walking speed, without
the use of a 3D Lidar
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