SACSoN: Scalable Autonomous Control for Social Navigation
- URL: http://arxiv.org/abs/2306.01874v3
- Date: Wed, 25 Oct 2023 20:25:41 GMT
- Title: SACSoN: Scalable Autonomous Control for Social Navigation
- Authors: Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine
- Abstract summary: We develop methods for training policies for socially unobtrusive navigation.
By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space.
We collect a large dataset where an indoor mobile robot interacts with human bystanders.
- Score: 62.59274275261392
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine learning provides a powerful tool for building socially compliant
robotic systems that go beyond simple predictive models of human behavior. By
observing and understanding human interactions from past experiences, learning
can enable effective social navigation behaviors directly from data. In this
paper, our goal is to develop methods for training policies for socially
unobtrusive navigation, such that robots can navigate among humans in ways that
don't disturb human behavior. We introduce a definition for such behavior based
on the counterfactual perturbation of the human: if the robot had not intruded
into the space, would the human have acted in the same way? By minimizing this
counterfactual perturbation, we can induce robots to behave in ways that do not
alter the natural behavior of humans in the shared space. Instantiating this
principle requires training policies to minimize their effect on human
behavior, and this in turn requires data that allows us to model the behavior
of humans in the presence of robots. Therefore, our approach is based on two
key contributions. First, we collect a large dataset where an indoor mobile
robot interacts with human bystanders. Second, we utilize this dataset to train
policies that minimize counterfactual perturbation. We provide supplementary
videos and make publicly available the largest-of-its-kind visual navigation
dataset on our project page.
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