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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 Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Bio-inspired tail oscillation enables robot fast crawling...
Shipeng Liu, Meghana Sagare, Shubham Patil, Feifei Qian · 2025-09-16 · via cs.RO updates on arXiv.org

Deformable substrates such as sand and mud present significant challenges for terrestrial robots due to complex robot-terrain interactions. Inspired by mudskippers, amphibious animals that naturally adjust their tail morphology and movement jointly to navigate such environments, we investigate how tail design and control can jointly enhance flipper-driven locomotion on granular media. Using a bio-inspired robot modeled after the mudskipper, we experimentally compared locomotion performance between idle and actively oscillating tail configurations. Tail oscillation increased robot speed by 67% and reduced body drag by 46%. Shear force measurements revealed that this improvement was enabled by tail oscillation fluidizing the substrate, thereby reducing resistance. Additionally, tail morphology strongly influenced the oscillation strategy: designs with larger horizontal surface areas leveraged the oscillation-reduced shear resistance more effectively by limiting insertion depth. Based on these findings, we present a design principle to inform tail action selection based on substrate strength and tail morphology. Our results offer new insights into tail design and control for improving robot locomotion on deformable substrates, with implications for agricultural robotics, search and rescue, and environmental exploration.