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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep 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Reinforcement Learning Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring 3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding Action Images: End-to-End Policy Learning via Multiview Video Generation Towards Generalizable Robotic Manipulation in Dynamic Environments General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters IROSA: Interactive Robot Skill Adaptation using Natural Language Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders Optimized Human-Robot Co-Dispatch Planning for Petro-Site Surveillance under Varying Criticalities MerNav: A Highly Generalizable Memory-Execute-Review Framework for Zero-Shot Object Goal Navigation From Instruction to Event: Sound-Triggered Mobile Manipulation Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay 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Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion Volumetric Ergodic Control RoboTAG: End-to-end Robot Configuration Estimation via Topological Alignment Graph TwinOR: Photorealistic Digital Twins of Dynamic Operating Rooms for Embodied AI Research Multimodal Diffusion Forcing for Forceful Manipulation X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance Multi-Modal Manipulation via Multi-Modal Policy Consensus AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving Constrained Decoding for Safe Robot Navigation Foundation Models FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving LLM-based Realistic Safety-Critical Driving Video Generation Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents Learning to Play Piano in the Real World Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI Convex Hulls of Reachable Sets
Connectivity Maintenance for Multi-Robot Systems Under Motion and Sensing Uncertainties Using Distributed ADMM-based Trajectory Planning
Akshay Shetty, Derek Knowles, Grace Xingxin Gao · 2020-12-18 · via cs.RO updates on arXiv.org

Inter-robot communication enables multi-robot systems to coordinate and execute complex missions efficiently. Thus, maintaining connectivity of the communication network between robots is essential for many multi-robot systems. In this paper, we present a trajectory planner for connectivity maintenance of a multi-robot system. We first define a weighted undirected graph to represent the connectivity of the system. Unlike previous connectivity maintenance works, we explicitly account for robot motion and sensing uncertainties while formulating the graph edge weights. These uncertainties result in uncertain robot positions which directly affect the connectivity of the system. Next, the algebraic connectivity of the weighted undirected graph is maintained above a specified lower limit using a trajectory planner based on a distributed alternating direction method of multipliers (ADMM) framework. Here we derive an approximation for the Hessian matrices required within the ADMM optimization step to reduce the computational load. Finally, simulation results are presented to statistically validate the connectivity maintenance of our trajectory planner.

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