Communication-Driven Robot Learning for Human-Robot Collaboration
| dc.contributor.author | Habibian, Soheil | en |
| dc.contributor.committeechair | Losey, Dylan Patrick | en |
| dc.contributor.committeemember | Leonessa, Alexander | en |
| dc.contributor.committeemember | Akbari Hamed, Kaveh | en |
| dc.contributor.committeemember | Williams, Ryan K. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2024-07-26T08:00:20Z | en |
| dc.date.available | 2024-07-26T08:00:20Z | en |
| dc.date.issued | 2024-07-25 | en |
| dc.description.abstract | The growing presence of modern learning robots necessitates a fundamental shift in design, as these robots must learn skills from human inputs. Two main components close the loop in a human-robot interaction: learning and communication. Learning derives robot behaviors from human inputs, and communication conveys information about the robot's learning to the human. This dissertation focuses on methods that enable robots to communicate their internal state clearly while learning precisely from human inputs. We first consider the information implicitly communicated by robot behavior during human interactions and whether it can be utilized to form human-robot teams. We investigate behavioral economics to identify biases and expectations in human team dynamics and incorporate them into human-robot teams. We develop and demonstrate an optimization approach that relates high-level subtask allocations to low-level robot actions, which implicitly communicates learning to encourage human participation in robot teams. We then study how communication helps humans teach tasks to robots using active learning and interactive imitation learning algorithms. Within the active learning approach, we develop a model that forms a belief over the human's mental model about the robot's learning. We validate that our algorithm enables the robot to balance between learning human preferences and implicitly communicating its learning through questions. Within the imitation learning approach, we integrate a wrapped haptic display that explicitly communicates representations from the robot's learned behavior to the user. We show that our framework helps the human teacher improve different aspects of the robot's learning during kinesthetic teaching. We then extend this system to a more comprehensive interactive learning architecture that provides multi-modal feedback through augmented reality and haptic interfaces. We present a case study with this closed-loop system and illustrate improved teaching, trust, and co-adaptation as the measured benefits of communicating robot learning. Overall, this dissertation demonstrates that bi-directional communication helps robots learn faster and adapt better, while humans experience a more intuitive and trust-based interaction. | en |
| dc.description.abstractgeneral | The growing presence of modern learning robots necessitates a fundamental shift in design, as these robots must learn skills from human inputs. This dissertation focuses on methods that enable robots to communicate their internal state clearly while learning precisely from human inputs. We first consider how robot behaviors during human interactions can be used to form human-robot teams. We investigate human-human teams in behavioral economics to better understand human expectations in human-robot teams. We develop a model that enables robots to distribute subtasks in a way that encourages their human partner to keep collaborating with them. We then study how communication helps human-in-the-loop robot teaching. Within active learning, we develop a model that infers what the human thinks about the robot's learning. We validate that, with our algorithm, the robot efficiently learns human preferences and keeps the human updated about what it has learned. Within imitation learning, we integrate a haptic device that explicitly communicates features from the robot's learned behavior to the user. We show that our framework helps users effectively improve their kinesthetic teaching. We then extend this system to a more comprehensive interactive robot learning architecture that provides feedback through augmented reality and haptic interfaces. We conduct a case study and illustrate that our framework improves robot teaching, human trust, and human-robot co-adaptation. Overall, this dissertation demonstrates that bi-directional communication helps robots learn faster and adapt better, while humans experience a more intuitive and trust-based interaction. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41211 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120698 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Robot Learning | en |
| dc.subject | Imitation Learning | en |
| dc.subject | Human-Robot Interaction | en |
| dc.subject | Machine Learning | en |
| dc.subject | Artificial Intelligence | en |
| dc.title | Communication-Driven Robot Learning for Human-Robot Collaboration | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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