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We present a distributed Bayesian algorithm for robot swarms to classify a spatially distributed feature of an environment. This type of “go/no-go” decision appears in applications where a group of robots must collectively choose whether to take action, such as determining if a farm field should be treated for pests. Previous bio-inspired approaches to decentralized decision-making in robotics lack a statistical foundation, while decentralized Bayesian algorithms typically require a strongly connected network of robots. In contrast,our algorithm allows simple, sparsely distributed robots to quickly reach accurate decisions about a binary feature of their environment. We investigate the speed vs. accuracy tradeoff in decision-making by varying the algorithm’s parameters.We show that making fewer, less-correlated observations can improve decision-making accuracy, and that a well-chosen combination of prior and decision threshold allows for fast decisions with a small accuracy cost. Both speed and accuracy also improved with the addition of bio-inspired positive feed-back. This algorithm is also adaptable to the difficulty of the environment. Compared to a fixed-time benchmark algorithm with accuracy guarantees, our Bayesian approach resulted in equally accurate decisions, while adapting its decision time to the difficulty of the environment. 

This study examines human control of physical interaction with objects that exhibit complex (nonlinear, chaotic, underactuated) dynamics. We hypothesized that humans exploited stability properties of the human-object interaction. Using a simplified 2D model for carrying a “cup of coffee”, we developed a virtual implementation to identify human control strategies. Transporting a cup of coffee was modeled as a cart with a suspended pendulum, where humans moved the cart on a horizontal line via a robotic manipulandum. The specific task was to transport the cart-pendulum system to a target, as fast as possible, while accommodating assistive and resistive perturbations. To assess trajectory stability, we applied contraction analysis. We showed that when the perturbation was assistive, humans absorbed the perturbation by controlling cart trajectories into a contraction region prior to the perturbation. When the perturbation was resistive, subjects passed through a contraction region following the perturbation. Entering a contraction region stabilizes performance and makes the dynamics more predictable. This human control strategy could inspire more robust control strategies for physical interaction in robots. 

This study examines human control of physical interaction with objects that exhibit complex (nonlinear, chaotic, underactuated) dynamics. We hypothesized that humans exploited stability properties of the human-object interaction. Using a simplified 2D model for carrying a “cup of coffee”, we developed a virtual implementation to identify human control strategies. Transporting a cup of coffee was modeled as a cart with a suspended pendulum, where humans moved the cart on a horizontal line via a robotic manipulandum. The specific task was to transport the cart-pendulum system to a target, as fast as possible, while accommodating assistive and resistive perturbations. To assess trajectory stability, we applied contraction analysis. We showed that when the perturbation was assistive, humans absorbed the perturbation by controlling cart trajectories into a contraction region prior to the perturbation. When the perturbation was resistive, subjects passed through a contraction region following the perturbation. Entering a contraction region stabilizes performance and makes the dynamics more predictable. This human control strategy could inspire more robust control strategies for physical interaction in robots.