Learning from Demonstration (LfD) is a powerful method for nonroboticists
end-users to teach robots new tasks, enabling them to
customize the robot behavior. However, modern LfD techniques do
not explicitly synthesize safe robot behavior, which limits the deployability
of these approaches in the real world. To enforce safety
in LfD without relying on experts, we propose a new framework,
ShiElding with Control barrier fUnctions in inverse REinforcement
learning (SECURE), which learns a customized Control Barrier
Function (CBF) from end-users that prevents robots from taking
unsafe actions while imposing little interference with the task completion.
We evaluate SECURE in three sets of experiments. First,
we empirically validate SECURE learns a high-quality CBF from
demonstrations and outperforms conventional LfD methods on simulated
robotic and autonomous driving tasks with improvements
on safety by up to 100%. Second, we demonstrate that roboticists
can leverage SECURE to outperform conventional LfD approaches
on a real-world knife-cutting, meal-preparation task by 12.5% in
task completion while driving the number of safety violations to
zero. Finally, we demonstrate in a user study that non-roboticists
can use SECURE to efectively teach the robot safe policies that
avoid collisions with the person and prevent cofee from spilling.
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Transparent Learning from Demonstration for Robot-Mediated Therapy
Robot-mediated therapy is an emerging field of research seeking to improve therapy for children with Autism Spectrum Disorder (ASD). Current approaches to autonomous robot-mediated therapy often focus on having a robot teach a single skill to children with ASD and lack a personalized approach to each individual. More recently, Learning from Demonstration (LfD) approaches are being explored to teach socially assistive robots to deliver personalized interventions after they have been deployed but these approaches require large amounts of demonstrations and utilize learning models that cannot be easily interpreted. In this work, we present a LfD system capable of learning the delivery of autism therapies in a data-efficient manner utilizing learning models that are inherently interpretable. The LfD system learns a behavioral model of the task with minimal supervision via hierarchical clustering and then learns an interpretable policy to determine when to execute the learned behaviors. The system is able to learn from less than an hour of demonstrations and for each of its predictions can identify demonstrated instances that contributed to its decision. The system performs well under unsupervised conditions and achieves even better performance with a low-effort human correction process that is enabled by the interpretable model.
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- Award ID(s):
- 1948224
- NSF-PAR ID:
- 10442497
- Date Published:
- Journal Name:
- 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
- Page Range / eLocation ID:
- 891 to 897
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RO-MAN53752.2022.9900854
