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Recent research overview talks. Left: TU Berlin; right: Talking Robotics.
My goal is to enable robots to enhance productivity by assisting and collaborating with humans in dynamic, multiagent environments like manufacturing sites, warehouses, and hospitals. These environments are complex: humans perform a variety of time-critical tasks, ranging from machine operation to inspection. To be truly helpful, robots need to account for human safety, comfort, and efficiency as they complete their own tasks. My research contributes a variety of tools to approach this goal, including planning and prediction algorithms informed by mathematical insights and models of human behavior, intuitive human-AI interfaces, and highly dexterous robot hardware, all of which are evaluated in extensive experiments with human subjects.
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Crowd navigation experiments. Left: video from my crowd navigation user study at Cornell [HRI19]. Right: instance from my experimnents at UW, featuring the HONDA P.A.T.H.Bot [preprint21a].
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We employ topological braids to model complex multiagent behavior. On the left: a planning framework that employs topological braids to account for cooperative collision avoidance in discrete worlds [IJRR19]. On the right: braids can succinctly summarize complex real-world traffic. [ICRA22].
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Algorithmic frameworks leveraging topological representations for modeling multiagent dynamics. On the left: a planning framework that treats decentralized navigation as implicit communication of topological information, encoded as braids in traffic scenes [WAFR22]. On the right: a prediction architecture that conditions trajectory reconstruction on likelihood of topological modes, identified using winding numbers. [CoRL20].
Our active-learning framework for modeling a map of robot trajectories to likely behavioral attributions from a human observer [CoRL21b][project website].
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Asking for help. On the left: planning under uncertainty to determine when to ask a user for localization help [RSS21]. On the right: a system that wanders real-world environments by leveraging human help [HRI22].
Many important tasks like assembly and delivery require dexterous robots, capable of robustly interacting with their environment, autonomously or through human feedback. My research has looked at the design of underactuated mechanisms [IROS14] whose capabilities can be enhanced through the incorporation of braking technology [IROS15][preprint22], the development of grasp planning algorithms [ICRA13][ICRA14], and sensing technologies [IROS22]. This research has been informed by work on understanding of human dexterity [IROS20][IROS15]. On many occasions, human situational awareness can augment robot capabilities, as we showed in complex tasks like chopstick manipulation [IROS20]. In fact, humans have developed sophisticated nonprehensile manipulation strategies like pushing to complete complex tasks in the real world. Inspired by humans, we built a multirobot pushing system that is capable of rearranging cluttered workspaces [preprint].
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Human-inspired manipulation. Left: human situational awareness enables the completion of challenging tasks like chopstick manipulation through a teleoperation system [IROS20]. Right: a multirobot system that generates push-based manipulation plans to reconfigure cluttered workspaces [preprint].
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Enhancing dexterity via braking technology. Left: electrostatic braking empowers a 10-link robot to perform complex manipulation maneuvers [preprint22]. Right: A brake-equipped underactuated hand is capable of performing complex rolling tasks, leveraging electrostatic braking and proximity sensing [IROS22].