1,012 research outputs found
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
Collision avoidance algorithms are essential for safe and efficient robot
operation among pedestrians. This work proposes using deep reinforcement (RL)
learning as a framework to model the complex interactions and cooperation with
nearby, decision-making agents, such as pedestrians and other robots. Existing
RL-based works assume homogeneity of agent properties, use specific motion
models over short timescales, or lack a principled method to handle a large,
possibly varying number of agents. Therefore, this work develops an algorithm
that learns collision avoidance among a variety of heterogeneous,
non-communicating, dynamic agents without assuming they follow any particular
behavior rules. It extends our previous work by introducing a strategy using
Long Short-Term Memory (LSTM) that enables the algorithm to use observations of
an arbitrary number of other agents, instead of a small, fixed number of
neighbors. The proposed algorithm is shown to outperform a classical collision
avoidance algorithm, another deep RL-based algorithm, and scales with the
number of agents better (fewer collisions, shorter time to goal) than our
previously published learning-based approach. Analysis of the LSTM provides
insights into how observations of nearby agents affect the hidden state and
quantifies the performance impact of various agent ordering heuristics. The
learned policy generalizes to several applications beyond the training
scenarios: formation control (arrangement into letters), demonstrations on a
fleet of four multirotors and on a fully autonomous robotic vehicle capable of
traveling at human walking speed among pedestrians.Comment: arXiv admin note: substantial text overlap with arXiv:1805.0195
Socially Aware Motion Planning with Deep Reinforcement Learning
For robotic vehicles to navigate safely and efficiently in pedestrian-rich
environments, it is important to model subtle human behaviors and navigation
rules (e.g., passing on the right). However, while instinctive to humans,
socially compliant navigation is still difficult to quantify due to the
stochasticity in people's behaviors. Existing works are mostly focused on using
feature-matching techniques to describe and imitate human paths, but often do
not generalize well since the feature values can vary from person to person,
and even run to run. This work notes that while it is challenging to directly
specify the details of what to do (precise mechanisms of human navigation), it
is straightforward to specify what not to do (violations of social norms).
Specifically, using deep reinforcement learning, this work develops a
time-efficient navigation policy that respects common social norms. The
proposed method is shown to enable fully autonomous navigation of a robotic
vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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