Titlebook: Algorithmic Foundations of Robotics XII; Proceedings of the T Ken Goldberg,Pieter Abbeel,Lauren Miller Book 2020 Springer Nature Switzerlan

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Cloud-based Motion Plan Computation for Power-Constrained Robots, robot the ability to adapt to changes between updates from the server. In our results, we show that with typical latency and bandwidth limitations, our method gains significant improvement in the responsiveness and quality of motion plans in interactive scenarios.

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Generating Plans that Predict Themselves,ng robot plan from the overall task goal and the observed initial . actions in the plan. We contribute a method for generating .-predictable plans: we search for a full plan that accomplishes the task, but in which the first . actions make it as easy as possible to infer the remaining ones. The resu

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,You Can’t Save all the Pandas: Impossibility Results for Privacy-Preserving Tracking, to the information available to the robot at the start — conditions we call initial I-state dependent cases. Quite naturally in the one-dimensional model, one may quantify sensing power by the number of perceptual (or output) classes available to the robot. The number of initial I-state dependent c

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Approximation Algorithms for Tours of Height-varying View Cones,n agriculture application. We further study its performance in simulation using randomly generated cone sets. Our results indicate that the performance of our algorithm is superior to standard solutions.

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Reactive Motion Planning in Uncertain Environments via Mutual Information Policies, on mutual information, returns a complete policy and a bound on the gap between the policy’s expected cost and the optimal. Finally, simulations are run on a flexible factory scenario to demonstrate the scalability of the proposed approach.

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