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Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning EquiDexFlow: Contact-Grounded SE(3)-Equivariant Dexterous Grasp Generative Flows FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation Planning with the Views via Scene Self-Exploration Lifted Schrödinger Bridges for Gaussian Mixture Endpoints: Projection Gaps and Path-Space Obstructions Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios Goal-Conditioned Decision Transformer for Multi-Goal Offline Reinforcement Learning BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes 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A Collaborative Team of UAV-Hexapod for an Autonomous Retrieval System in GNSS-Denied Maritime Environments
Seungwook Lee, Maulana Bisyir Azhari, Gyuree Kang, Ozan Günes, D · 2024-10-13 · via cs.RO updates on arXiv.org

We present an integrated UAV-hexapod robotic system designed for GNSS-denied maritime operations, capable of autonomous deployment and retrieval of a hexapod robot via a winch mechanism installed on a UAV. This system is intended to address the challenges of localization, control, and mobility in dynamic maritime environments. Our solution leverages sensor fusion techniques, combining optical flow, LiDAR, and depth data for precise localization. Experimental results demonstrate the effectiveness of this system in real-world scenarios, validating its performance during field tests in both controlled and operational conditions in the MBZIRC 2023 Maritime Challenge.

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