Titlebook: Robotics Research; Aude Billard,Tamim Asfour,Oussama Khatib Conference proceedings 2023 The Editor(s) (if applicable) and The Author(s), u

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Learning Long-Horizon Robot Exploration Strategies for Multi-object Search in Continuous Action Spacthods still struggle with long-horizon tasks and require large amounts of data to generalize to unseen environments. In this work, we present a novel reinforcement learning approach for multi-object search that combines short-term and long-term reasoning in a single model while avoiding the complexi

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Monocular Camera and Single-Beam Sonar-Based Underwater Collision-Free Navigation with Domain Random poor visibility. Furthermore, good-quality obstacle detection sensors for underwater robots are scant and costly; and many sensors like RGB-D cameras and LiDAR only work in-air. To enable reliable mapless underwater navigation despite these challenges, we propose a low-cost end-to-end navigation sy

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Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraintse actions while considering this problem is not trivial. State of the art approaches therefore use multi-modal hypotheses to represent the states of the agent and of the environment. However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the

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6N-DoF Pose Tracking for Tensegrity Robotsding flexibility, low weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the state estima

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