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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
Impact of Extended Reality on Robot-Assisted Surgery Trai...
Michael Bickford, Fayez Alruwaili, Sara Ragab, Hanna Rothenberg, · 2025-01-23 · via cs.RO updates on arXiv.org

Robot Assisted Surgeries (RAS) have one of the steepest learning curves of any type of surgery. Because of this, methods to practice RAS outside the operating room have been developed to improve the surgeons skills. These strategies include the incorporation of extended reality simulators into surgical training programs. In this Systematic review, we seek to determine if extended reality simulators can improve the performance of novice surgeons and how their performance compares to the conventional training of surgeons on Surgical robots. Using the PRISMA 2020 guidelines, a systematic review and meta-analysis was performed searching PubMed, Embase, Web of Science, and Cochrane library for studies that compared the performance of novice surgeons that received no additional training, trained with extended reality, or trained with inanimate physical simulators (conventional additional training). We included articles that gauged performance using either GEARS or Time to complete measurements and used SPSS to perform a meta-analysis to compare the performance outcomes of the surgeons after training. Surgeons trained using extended reality completed their surgical tasks statistically significantly faster than those who did not receive training (Cohen's d=-0.95, p=0.02), and moderately slower than those conventionally trained (Cohen's d=0.65, p=0.14). However, this difference was not statistically significant. Surgeons trained on extended reality demonstrated a statistically significant improvement in GEARS scores over those who did not train (Cohen's d=0.964, p<0.001). While surgeons trained in extended reality had comparable GEARS scores to surgeons trained conventionally (Cohen's d=0.65, p=0.14). This meta-analysis demonstrates that extended reality simulators translated complex skills to surgeons in a low cost and low risk environment.