A Distributed Pipeline for Scalable, Deconflicted Formation Flying

Reliance on external localization infrastructure and centralized coordination are main limiting factors for formation flying of vehicles in large numbers and in unprepared environments. While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability. To address these shortcomings, we present a unified pipeline with onboard localization and a distributed, collision-free motion planning strategy that scales to a large number of vehicles. Since distributed collision avoidance strategies are known to result in gridlock, we also present a decentralized task assignment solution to deconflict vehicles. We experimentally validate our pipeline in simulation and hardware. The results show that our approach for solving the optimization problem associated with motion planning gives solutions within seconds in cases where general purpose solvers fail due to high complexity. In addition, our lightweight assignment strategy leads to successful and quicker formation convergence in 96-100% of all trials, whereas indefinite gridlocks occur without it for 33-50% of trials. By enabling large-scale, deconflicted coordination, this pipeline should help pave the way for anytime, anywhere deployment of aerial swarms.Comment: 8 main pages, 1 additional page, accepted to RA-L and IROS'2

Robotic Assistance in Coordination of Patient Care

We conducted a study to investigate trust in and dependence upon robotic decision support among nurses and doctors on a labor and delivery floor. There is evidence that suggestions provided by embodied agents engender inappropriate degrees of trust and reliance among humans. This concern is a critical barrier that must be addressed before fielding intelligent hospital service robots that take initiative to coordinate patient care. Our experiment was conducted with nurses and physicians, and evaluated the subjects’ levels of trust in and dependence on high- and low-quality recommendations issued by robotic versus computer-based decision support. The support, generated through action-driven learning from expert demonstration, was shown to produce high-quality recommendations that were ac- cepted by nurses and physicians at a compliance rate of 90%. Rates of Type I and Type II errors were comparable between robotic and computer-based decision support. Furthermore, em- bodiment appeared to benefit performance, as indicated by a higher degree of appropriate dependence after the quality of recommendations changed over the course of the experiment. These results support the notion that a robotic assistant may be able to safely and effectively assist in patient care. Finally, we conducted a pilot demonstration in which a robot assisted resource nurses on a labor and delivery floor at a tertiary care center.National Science Foundation (U.S.) (Grant 2388357

Policy Distillation and Value Matching in Multiagent Reinforcement Learning

© 2019 IEEE. Multiagent reinforcement learning (MARL) algorithms have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works have focused on sharing information between agents via centralized critics to stabilize learning or through communication to improve performance, but do not generally consider how information can be shared between agents to address the curse of dimensionality in MARL. We posit that a multiagent problem can be decomposed into a multi-task problem where each agent explores a subset of the state space instead of exploring the entire state space. This paper introduces a multiagent actor-critic algorithm for combining knowledge from homogeneous agents through distillation and value-matching that outperforms policy distillation alone and allows further learning in discrete and continuous action spaces

Robotic assistance in the coordination of patient care

We conducted a study to investigate trust in and dependence upon robotic decision support among nurses and doctors on a labor and delivery floor. There is evidence that suggestions provided by embodied agents engender inappropriate degrees of trust and reliance among humans. This concern represents a critical barrier that must be addressed before fielding intelligent hospital service robots that take initiative to coordinate patient care. We conducted our experiment with nurses and physicians, and evaluated the subjects’ levels of trust in and dependence upon high- and low-quality recommendations issued by robotic versus computer-based decision support. The decision support, generated through action-driven learning from expert demonstration, produced high-quality recommendations that were accepted by nurses and physicians at a compliance rate of 90%. Rates of Type I and Type II errors were comparable between robotic and computer-based decision support. Furthermore, embodiment appeared to benefit performance, as indicated by a higher degree of appropriate dependence after the quality of recommendations changed over the course of the experiment. These results support the notion that a robotic assistant may be able to safely and effectively assist with patient care. Finally, we conducted a pilot demonstration in which a robot-assisted resource nurses on a labor and delivery floor at a tertiary care center.National Science Foundation Graduate Research Fellowship Program (Grant 23883577