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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
- 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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Robot-mediated interventions have been investigated for the treatment of social skill deficits amongst children with Autism Spectrum Disorder (ASD). Does the use of a Nao robot as a mediator increase vocal interaction between children with ASD? The present study examined the vocalization and turn-taking rate in six children with ASD (mean age = 11.4 years, SD = 0.86 years) interacting with and without a Nao robot for 10 sessions, order counterbalanced. Each session lasted nine minutes. In the Robot condition, the robot provided vocal prompts; in the No Robot condition, children interacted freely. Child vocalization and turn-taking rate defined as the number of utterances/turns per second were measured. Results demonstrated that three children produced higher vocalization and turn-taking rates when a robot was present, and two when it was absent. One participant produced higher vocalization rates when the robot was not present, but more conversational turns when the robot was present. The findings suggest that the use of a Nao robot as a social mediator increases vocalization and turn-taking rates among children with ASD, but large individual variability is observed. The effect of the robot as a mediator on lexical diversity of child speech will also be investigated.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RO-MAN53752.2022.9900854
