Heterogeneous Learning from Demonstration
- URL: http://arxiv.org/abs/2001.09569v2
- Date: Tue, 14 Apr 2020 19:29:24 GMT
- Title: Heterogeneous Learning from Demonstration
- Authors: Rohan Paleja, Matthew Gombolay
- Abstract summary: We propose a framework for learning from heterogeneous demonstration based upon Bayesian inference.
We evaluate a suite of approaches on a real-world dataset of gameplay from StarCraft II.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The development of human-robot systems able to leverage the strengths of both
humans and their robotic counterparts has been greatly sought after because of
the foreseen, broad-ranging impact across industry and research. We believe the
true potential of these systems cannot be reached unless the robot is able to
act with a high level of autonomy, reducing the burden of manual tasking or
teleoperation. To achieve this level of autonomy, robots must be able to work
fluidly with its human partners, inferring their needs without explicit
commands. This inference requires the robot to be able to detect and classify
the heterogeneity of its partners. We propose a framework for learning from
heterogeneous demonstration based upon Bayesian inference and evaluate a suite
of approaches on a real-world dataset of gameplay from StarCraft II. This
evaluation provides evidence that our Bayesian approach can outperform
conventional methods by up to 12.8$%$.
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