Bayes-Swarm is a new decentralized swarm robotic search method using trajectory-based knowledge modeling, time-adaptive exploration/exploitation balancing, and local penalization in batch BO, showing up to 76x efficiency over exhaustive search on multimodal signals.
Gaussian processes in machine learning,
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Semi-parametric Gaussian process regression yields the most accurate inverse dynamics models in most tested robotic scenarios compared to parametric, non-parametric, and other semi-parametric baselines.
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Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search
Bayes-Swarm is a new decentralized swarm robotic search method using trajectory-based knowledge modeling, time-adaptive exploration/exploitation balancing, and local penalization in batch BO, showing up to 76x efficiency over exhaustive search on multimodal signals.
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Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics
Semi-parametric Gaussian process regression yields the most accurate inverse dynamics models in most tested robotic scenarios compared to parametric, non-parametric, and other semi-parametric baselines.