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#Perception and localization
Oct 21Wed
  1. arXiv cs.RO46

    ThERMO Thermal Imaging Speed Optimization Algorithm Improves UR10e Robot Autonomous Tumor Resection Accuracy

    The ThERMO closed-loop thermal feedback algorithm completed autonomous electrosurgical tests on ex vivo porcine tissue using the UR10e-based ASTR system, reducing thermal injury by up to 52% and improving margin accuracy by up to 59%, without requiring data-driven training or iterative tuning. The algorithm adjusts cutting speed via real-time thermal imaging; histological analysis and 3D photogrammetry verified thermal injury extent and margin accuracy, outperforming two fixed-speed control methods.

  2. arXiv cs.RO47

    Real-Time Disturbance-Aware Koopman Model Predictive Control for Quadrotors in Spatially Varying Winds

    The study proposes a disturbance-aware Koopman control family (DA-KCF) framework that preserves an offline-identified nominal model, learns only a disturbance correction online, and embeds it into model predictive control (MPC) to optimize position and yaw references. The reference-dependent KCF is first learned from simulation data and then calibrated with limited hardware data; during deployment the nominal model remains fixed, and a lightweight residual ensemble learns a low-dimensional disturbance correction from causal state-command-state transitions.

  3. arXiv cs.RO45

    RadGeoLoc: A Particle Filtering-Based Radar Geolocalization Method

    RadGeoLoc proposes a geolocalization method that aligns ground-based spinning radar measurements with satellite RGB imagery for autonomous localization when GNSS is unreliable. The method uses a radar representation pretrained via masked reconstruction, aligns it with satellite image features, proposes candidate poses through a particle filter driven by radar odometry, and derives likelihoods from a learned observation model. Evaluation covers 8 datasets and 31 distinct routes spanning urban and offroad conditions, and examines robustness under changing weather through repeated traversals of the same route over 13 months, using only ground radar and a coarse local prior.

  4. arXiv cs.RO55

    Failure Attribution for VLA Models: Estimating the Benefit of Removing Visual Corruptions

    The paper defines the problem of failure attribution for vision-language-action (VLA) models: when multiple visual corruptions coexist, determining which corruptions contribute to failure risk and how much benefit their removal would bring. The authors propose a factorized risk model that learns separate representations of the scene and of corruptions, and estimates removal benefits through latent intervention using only the current image and task instruction, without paired clean images or additional counterfactual rollouts.

  5. arXiv cs.RO45

    Theoretical Analysis of the Relationship Between Maximum Clique and Maximum Consensus in Point Cloud Registration

    arXiv:2610.27275 studies the relationship between maximum clique and maximum consensus, two robust registration formulations for point cloud registration. It proves that the alignment error of maximum clique solutions can be arbitrarily large and that the set size can far exceed that of the maximum consensus set, and it derives geometric conditions that bound this error. When rotation is fixed, it gives exact error bounds and a discriminating test for the translation subproblem. The study also determines the minimum group size in group-k consistency that guarantees an error bound independent of point cloud scale: d+1 for rotation and translation in d≥2 dimensions, and d+2 when reflections are allowed. An interactive example and Lean theorem prover verification have been made public.

  7. arXiv cs.RO41

    Visual Task Modeling and Automated Planning for Nao Robots in Autism Spectrum Disorder Education

    A method for autism spectrum disorder (ASD) education enables a camera-equipped Nao robot, using a vision-based large language model, to convert photos of a Shoebox Task into a Planning Domain Definition Language (PDDL) representation and generate action plans, autonomously executing the task, prompting the next step, or reporting failures through action demonstrations, voice, and gestures.

  8. arXiv cs.RO44

    Research on Interaction State Recognition for Gecko-Inspired Adhesive Interfaces for On-Orbit Servicing

    A study embeds capacitive sensing into a gecko-inspired dry adhesive interface to recognize five interaction states in real time during on-orbit servicing grasps (idle, proximity, contact, engaged, disaged). Random Forest achieved 95.65% offline classification accuracy; online, combined with a rule-based state machine, accuracy rose from 78.02% to 93.14%, with a macro F1 of 0.9343 and an inference cycle of 53 ms.

  9. arXiv cs.RO56

    ACE-Brain-0.6 Released: Scaling Spatial Perception of a 9B Embodied Model with Verifiable Grounding Supervision

    The research team released ACE-Brain-0.6, which uses a unified data pipeline to combine robot manipulation recordings with complementary visual data from multiple sources, automatically annotates them, and converts the result into verifiable grounding supervision; the model was obtained by fine-tuning Qwen3.5-9B on approximately 1.5 million samples. ACE-Brain-0.6 outperforms GPT-6 Astra on four of seven public grounding benchmarks, with an overall average score of 75.41 versus 73.68; under a fixed fine-tuning setup, overall grounding performance improves steadily as the dataset expands to cover more grounding scenarios.

  12. Fudan Multimodal Embodied Intelligence38

    Fudan University's Qi Lizhe team wins innovation gold award at 26th CIIF university exhibition area for "space-air-ground integrated desert planting robot"

    The "space-air-ground integrated desert planting robot" project from Fudan University's Qi Lizhe team won the innovation gold award in the university exhibition area at the 26th China International Industry Fair. The robot integrates autonomous perception, precise navigation, and integrated planting functions, combining BeiDou, inertial navigation, LiDAR, and stereo vision positioning, and has completed field operation verification in the desert of Dalad Banner, Ordos, Inner Mongolia. The team has filed 7 core invention patents, and the single-vehicle platform will later integrate four planting mechanisms, with operating efficiency reaching more than 10 times that of traditional manual work.

Oct 20Tue
  4. 3D视觉工坊 · 微信公众号38

    Jingxun V4 Research-Grade Area Structured Light Scanner: 14-megapixel camera + CUDA-accelerated point cloud processing

    Suzhou sandisiwei has launched the Jingxun V4 research-grade area structured light scanner, featuring a measurement-grade DLP4710 optical engine, interchangeable C-mount lenses, and a Hikvision 14-megapixel global camera, with CUDA acceleration for the entire point cloud processing workflow. The product supports single-point measurement, regional flatness/sphericity calculation, manual and feature-based point cloud stitching, and SSD surface reconstruction. It comes with structured light 3D reconstruction and PCL point cloud processing courses, is open at the source-code level, and supports secondary development with OpenCV, Halcon, and VisionMaster.

  5. 3D视觉工坊 · 微信公众号57

    CVPR 2026 Highlight: Generating multi-sensor images aligned with RGB views without calibration or depth

    3D Vision Workshop introduces a CVPR 2026 Highlight work that proposes a framework named 3D-RGBX, which can synthesize thermal, near-infrared, and SAR images aligned at the pixel level with RGB views by running COLMAP only on RGB, without requiring metric depth, pose, intrinsics, or calibration of the X sensor.

  6. MassRobotics55

    Algorized and ZHAW Launch Research Project on Predicting Human Movement Around Collaborative Robots

    Algorized and the ZHAW Zurich University of Applied Sciences Institute of Signal Processing and Wireless Communications (ISC) have launched a two-year research project, Real-Time Human Perception via Edge-AI Wireless Sensor Fusion for Collaborative Robotics, funded by InnoSuisse and starting in September 2026.

  8. 深蓝AI · 微信公众号72

    Zhejiang University's Gao Fei team releases CrossFly benchmark: evaluation of cross-indoor-outdoor interactive navigation for drones and a hierarchical VLA baseline

    The Gao Fei team at Zhejiang University, together with Weifen Zhifei, launched the CrossFly benchmark, building 23 reconstructed real-world scenes and 15,263 complete trajectories, covering three task types: indoor, outdoor, and indoor-outdoor cross-domain, accompanied by a hierarchical VLA baseline, CrossFly-Agent; the full simulation dataset, evaluation protocol, and baseline code are all open source.

  9. Reddit r/robotics38

    Design discussion for a tiny RGB-D recorder: comparing stereo, dToF, and iToF depth performance in hand-object scenes

    A Reddit user proposed designing a tiny RGB + depth recorder for hand-object manipulation demonstrations and compared single-frame model outputs from three depth approaches in the same synthetic scene (a chest-view grasp of a mug): a D405-class stereo camera, the TMF8829 dToF (48 × 32 zones), and a generic iToF (240 × 180).

  10. 电子工程专辑 · 微信公众号42

    The underlying logic behind the humanoid robot boom: Analog Devices' 36-page special report explains perception, control, and safety in depth

    Analog Devices, together with EE Times China, has released an October special report, "[Engineer Must-Read] A Guide to Breaking Through the Full-Stack Robotics Technology Barrier," totaling 36 pages. Aimed at developers of AMRs, robotic arms, and humanoid robots, it is available for free download with no registration required.

  11. Robotics & Automation News56

    Toyota Material Handling says digital twins can halve autonomous forklift commissioning time

    Toyota Material Handling Europe is working with Nvidia and SoftServe on a simulation-first approach that completes testing and validation before equipment reaches customer sites, aiming to cut commissioning effort by up to 50% and shorten time to market for autonomous material handling systems by up to 40%.

  12. HuggingFace Daily Papers55

    Holi-World: Building Holistic World Intelligence from Human First-Person Experience

    The study proposes the HOLI-WORLD integrated ecosystem, which connects experience collection, world representation, automated data construction, model learning, and independent evaluation. HOLIWORLD-Foundry combines static and dynamic perception to automatically construct world records, and HOLIWORLD-Data organizes approximately 100 self-collected scenes into approximately 80K QA pairs used to train HOLIWORLD-VL-4B; independently human-annotated held-out recordings form HOLIWORLD-Bench.

  13. HuggingFace Daily Papers55

    EgoXperience: Learning World-Aligned Egocentric Representations from Human Experience

    EgoXperience proposes a complete pipeline covering data collection, automatic annotation, unified modeling, and benchmark evaluation, aimed at jointly reconstructing observer motion, scene geometry, and hand-object interactions from egocentric video. At its core is the automatic annotation engine EgoXX-Engine, which combines geometrically verified object tracking with SLAM-based world alignment to produce the 50-hour dataset EgoX-Data-6M (with second-level task descriptions) and the 1-hour evaluation set EgoX-Bench.

  14. New Strategy Robotics34

    Industrial Embodied Intelligence: Enabling Mobile Robots to Understand Factory Sites Like Veteran Workers

    Agiwei Robotics has introduced a new three-dimensional embodied intelligence paradigm based on VLA (Vision-Language-Agent) technology, used for environmental understanding and autonomous decision-making by unmanned forklifts under complex industrial conditions. Its self-developed fully dynamic laser SLAM navigation fuses LiDAR and 3D vision, achieving repeat positioning accuracy of ±5 mm; the fourth-generation MCU controller coordinates with high-precision servo motors for motion control. Agiwei's mobile robot solutions have entered industries including photovoltaic new energy, petrochemicals, and automotive parts, with cumulative delivery to 900 customers and annual capacity of 3,000 units (sets).

  15. 机器人全球资讯 · 微信公众号31

    First Real-World Intelligent Robot Skills Exhibition (Nanjing Stop) Held on October 20, with 27 Jiangning Companies Including Estun and Zhongke Guiji Exhibiting

    The First Real-World Intelligent Robot Skills Exhibition (Nanjing Stop) was held on October 20 at Southeast University's Jiulonghu Campus, where ten typical application scenarios for intelligent manufacturing in Nanjing were released, showcasing equipment such as special robots, intelligent inspection robots, dexterous hands, and underwater operation robots.

Oct 19Mon
  1. CVHub · 微信公众号74

    XPeng and multiple universities propose 4B perception foundation model GroundingPI, unifying point, box, and text localization and validating action transfer

    Researchers from XPeng and Peking University, the University of Hong Kong, UC Berkeley, Princeton University, National University of Singapore, Tsinghua University, and Hong Kong University of Science and Technology (Guangzhou) jointly proposed a 4B perception foundation model, GroundingPI, which unifies point selection, boxes, text, document regions, and interface controls into a single spatial token output protocol. The architecture uses a MoonViT-V2 vision encoder and a Qwen3-4B language backbone, and training includes supervised fine-tuning and GRPO reinforcement post-training.

  5. arXiv cs.RO64

    GroundingPI releases 4B grounding foundation model, averaging 73.68% across 34 benchmarks

    GroundingPI is a 4B-parameter grounding foundation model that outputs points and boxes as quantized coordinates in a shared vocabulary; its training combines multimodal and spatial pretraining, supervised fine-tuning, and GRPO reinforcement learning. It averages 73.68% across 34 grounding benchmarks and 44 baselines, above GPT-6 Astra's 71.54%.

  6. arXiv cs.RO57

    GroundAnything: Balancing Parallel Decoding and Precise Visual Grounding with Blockwise Denoising

    GroundAnything is a 4-billion-parameter visual grounding foundation model that combines parallel decoding with precise object localization through blockwise denoising. Its training integrates pretraining on public datasets, direct conversion from autoregressive to diffusion, supervised fine-tuning, and GRPO-based reinforcement post-training.

  7. arXiv cs.RO57

    EgoTac: Predicting In-the-Wild Touch from Egocentric Vision

    EgoTac is a generalizable model that predicts rich tactile information directly from egocentric human videos. It was trained on more than 5.7 million image-tactile pairs covering both continuous force measurements and binary contact. Experiments show an average force error below 0.06N for in-domain prediction, and on out-of-domain contact prediction benchmarks it consistently outperforms the current state-of-the-art contact estimator. It also enables zero-shot prediction on unconstrained real-world videos; scaling analysis indicates that both data diversity and data volume steadily improve performance. The work was accepted to NeurIPS 2026.

  8. arXiv cs.RO55

    CorrelationFlow: A Training-Free LiDAR Scene Flow Estimation Method Based on BEV Correlation

    The paper proposes CorrelationFlow, which reformulates LiDAR scene flow estimation as a correlation maximization problem between Bird's-Eye View (BEV) images, replacing learnable parameters with a chain of image processing operations. This training-free method won second place in the unsupervised track of the Argoverse 2 Scene Flow Challenge (CVPR 2026). The test set covers five datasets and a variety of vehicle platforms and environments, and results show its performance can match or even surpass learning-based methods. The source code will be open-sourced.

  10. arXiv cs.RO47

    Evaluating Sensitivity of World-Action Models to Spatial Instruction Rewriting

    An evaluation conducted 864 rollouts of three world-action models across four RoboLab scenes, holding initial states and task goals fixed, and found that describing spatial relations using the reference object rather than the object to be moved lowered the stable placement success rate by an average of 12.2 percentage points. An independent test of the policies' language understanding identified relation reversals, and adding images disrupted otherwise correct text-only interpretations. Rollout analysis also distinguished spatial placement from instruction following: the stable placement rate fell from 43.2% at any time to 21.8% at episode end.

  11. arXiv cs.RO56

    Analytic Robot Self-Model: Learning Screw-Axis Kinematics for Rendering and Model-Based Control

    The paper proposes an explicit analytic robot self-model that requires neither deformation networks nor URDF/CAD templates. From multi-view images and joint readings, it obtains appearance and rigid link assignments, and independently identifies metric screw axes using sparse end-effector poses, yielding closed-form forward kinematics and an analytic Jacobian that can be extrapolated.