<p> While robotics has made considerable strides toward more robust and adaptive manipulation, perception, and planning, robots in the near future are unlikely to be as dexterous, competent, and versatile as human workers. Rather than try to create fully autonomous systems that accomplish tasks independently, a more practical approach is to construct robots that work alongside people. This allows human and robot workers to concentrate on the tasks for which they are each best suited, while simultaneously providing the capability to assist each other during tasks that one worker lacks the ability to complete independently in a safe or maximally proficient manner. Human-robot teaming advances have the potential to extend applications of autonomous robots well beyond their current, limited roles in factory automation settings. Much of modern robotics remains inapplicable in many domains where tasks are either too complex, beyond modern hardware limitations, too sensitive for non-human completion, or too flexible for static automation practices. In these situations human-robot teaming can be leveraged to improve the efficiency, quality-of-life, and safety of human partners.</p><p> In this thesis, I describe algorithms that can create collaborative robots that call provide assistance when useful, remove dull or undesirable responsibilities when possible, and assist with dangerous tasks when feasible. In doing so, I present a novel method for autonomously constructing hierarchical task networks that factor complex tasks in was that make theism approachable by modern planning and coordination algorithms. In particular, within these complex cooperative tasks I focus on facilitating collaboration between a lead worker and robotic assistant within a shared space, defining and investigating a class of actions I term supportive behaviors: actions that serve to reduce the cognitive or kinematic complexity of tasks for teammates. The majority of contributions within this work center around discovering, learning, and executing these types of behaviors in multi-agent domains with asymmetric authority. I provide an examination of supportive behavior learning and execution from the perspective of task and motion planning, as well as that of learning directly from interactions with humans. These algorithms provide a collaborative robot with the capability to anticipate the needs of a human teammate and proactively offer help as needed or desired. This work enables to creation of robots that provide tools just-in-time, robots that alter workspaces to make more optimal task orderings more obvious and more feasible, and robots that recognize when a user is delayed in a complex task and offer assistance.</p><p> Combining these algorithms provides a basis for a robot with both a capacity for rich task comprehension and a theory of mind about its collaborators, enabling methods to allow such a robot to leverage knowledge it acquires to transition between the role of learner, able assistant, and informative instructor during interactions with teammates.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10160856 |
| Date | 17 September 2016 |
| Creators | Hayes, Bradley |
| Publisher | Yale University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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