A digital fruit fly reconstructed from micro-CT scans achieves hexapod walking in MuJoCo; the tripod gait self-organizes from phase-oscillator phase-locking, with reflex correction rules and adhesion measurements added. Its compound-eye vision outputs 721 ommatidia per eye, and all conclusions come from complete simulations with fixed seeds.
Taiying Technology Group has built an embodied AI robot mountain-climbing training ground at the foot of Mount Tai, replicating the 3.8 km, more than 3,100-step terrain of the Mount Tai section from Hongmen to Zhongtianmen, with 11 test tracks for humanoid robots and quadruped robot dogs to test and tune for the first CMG World Robot Mount Tai Climbing Competition. The training ground includes special tracks such as low-recognition steps, steep, slippery, narrow steps, and S-shaped sharp-turn steps, focusing on assessing environmental perception, dynamic balance, and gait-switching capabilities. It has been selected as one of Shandong Province's first batch of key-supported regional hub training grounds.
COCO Matrix released the robot model CO01, which treats demonstrations, failed attempts, and real-time guidance as experience prompts in context, allowing adaptation to new tasks without updating weights. Under the same pretrained model, a roughly 1-minute demonstration achieved an average success rate of 52% on unseen tasks; fine-tuning to a similar level required about 180 demonstrations and about 9 hours. In real-robot evaluations with at most five attempts per task, cumulative success rate rose from 33% on the first attempt to 84% when retaining its own history, versus 56% without retaining history.
Shenmou Intelligent's "Dragon Claw" aerial-operations drone has won State Grid Power's "High-Stability UAV for Aerial Operations" project, using a drone-plus-robotic-arm-plus-dexterous-hand configuration to address foreign-object hazards on overhead transmission lines. The system identifies foreign objects such as bird nests through inspection, then uses the robotic arm and dexterous hand to carry out integrated detection, handling, and verification. Shenmou says its self-developed technologies include the drone and robotic arm bodies, a multi-redundant flight control system, target grasping control, dynamic visual guidance, and piezoelectric six-axis force sensors.
The Xbotics Embodied Intelligence Lab has released Part 2 of Lecture 6 in its "Twenty-One Lectures" series, explaining how to generate grasp candidates from object poses and complete the coordinate transformation from the camera frame to the robot base frame. The article breaks down five elements: grasp point, approach direction, opening width, safety offset, and orientation matching. It compares the Eye-in-Hand and Eye-to-Hand hand-eye calibration topologies and provides procedures for reachability checks, degradation under observation failure, and troubleshooting perception failures.
Agibot launched the safety-standard heavy-duty embodied robot Genie G2 Max on October 26, targeting depalletizing and palletizing, material transfer, and production-line feeding. It has a 20 kg single-arm payload, a 40 kg dual-arm rated payload, a 50 kg peak payload, and repeat positioning accuracy within ±0.5 mm. The robot uses 7-DOF force-controlled dual arms, has a vertical working range of 0.1 to 2.5 meters, features dual-battery hot swapping and autonomous recharging, and is compatible with the Genie Studio 2.0 embodied work platform. Agibot disclosed that in e-commerce warehousing scenarios its overall task success rate exceeds 99%, with 80 boxes depalletized and 75 boxes palletized per hour.
Atomrobot has launched a flexible parallel loading and unloading workstation for small new-energy lithium-battery parts, 3C electronic components, and small daily-chemical products, performing visual recognition and positioning, high-speed picking, and tray placement. The equipment requires no modification to the production line foundation, can start production with AC220V, and its graphical interface lets ordinary operators complete debugging; it also supports rapid changeover between products. The workstation has already served customers including CATL, BYD, Foxconn, Luxshare, and Sunwoda.
The first CMG World Robot Mount Tai Climbing Competition is about to begin. The course runs from Hongmen to Zhongtianmen and covers more than 3,100 natural stone steps, with steep slopes and sharp turns that become slippery under the influence of temperature, dew, and precipitation. It is designed to test the autonomous, stable mobility of humanoid and quadruped robots in unstructured mountain terrain. The event features humanoid and legged robot racing, loaded transport and exoskeleton assistance special events, and an all-terrain mobility pathfinding challenge for wheeled, tracked, and wheel-legged composite configurations.
Suzhou 3D-SW has launched the SD-M series three-in-one 3D camera, which uses active structured light technology and supports three modes: area scan, oscillating line scan, and fixed line scan. It can output high-quality point clouds and is suitable for indoor and outdoor high-precision measurement, robotic arm grasping, welding, spraying, and other scenarios. The series includes the standard SD-M2600 as well as the SD-M51000 and SD-M52000, available in color and monochrome versions. The company provides an official SDK and tutorials, and has demonstrated reconstruction cases such as weld seam recognition, reflective parts, and castings.
Shenmou Intelligence's Dragon Claw aerial work drone has won State Grid Power's "High-Stability UAV for Aerial Operations" project, using a drone-plus-robotic-arm-plus-dexterous-hand configuration to clear foreign-object hazards on overhead transmission lines. The system identifies foreign objects such as bird nests through inspection, then uses the robotic arm and dexterous hand to carry out removal and verification, forming an integrated discover-handle-verify workflow. Shenmou says it developed the flight control system, robotic arm and dexterous-hand grasping control, dynamic visual guidance, and piezoelectric six-axis force sensors in-house, and cites research estimating annual procurement demand of about 150 million to 200 million yuan per province.
General-purpose large models are beginning to take part in robot control. Models such as GPT-6 Astra can already understand complex instructions, recognize objects, and plan operation steps in tests, but fine manipulation still requires coordination with dedicated control systems. Approaches such as HexaShell and WorldScape-RSI explore a division of labor of "large-model decision-making plus specialized-model execution," and tests show some improvement in task performance, though the results mainly come from controlled environments. On data collection, head-mounted operation videos are quoted at about 100 to 300 yuan per hour, while complex real-machine data can reach about 1,000 yuan per hour.
beRobox has launched the 3D Dynamic Safety module for its PALTZ palletizing robots, mounting the detection device on the robotic arm so the protective zone adjusts dynamically with the robot's position. The module uses Sonair's ADAR One 3D ultrasonic sensor, which the company says is the first 3D ultrasonic sensor to receive SIL 2 / Pl d safety certification.
BioniX Technology has opened global pre-orders for Lumi, a global-edition embodied AI lamp robot positioned as a "life-like" companion smart partner. The product is equipped with millimeter-wave radar and multimodal AI, supporting proximity sensing, prolonged-sitting reminders, and absence detection, and can express multiple postures through light, movement, and voice. Beyond lighting, Lumi can remember users' preferred music and phrases and adapt to habits to form differentiated personalities, creating a unique lamp for each individual.
DeepMirror released a full-stack physical AI solution for the steel industry at the 2026 Unitree Technology Partner Ecosystem Conference held in Hangzhou on October 22. Using Unitree quadruped robot dogs as the carrier and paired with its self-developed Insight series spatial intelligence cameras, the Navcore spatial intelligence brain, and a task management platform, the solution enables a complete inspection chain covering pure vision positioning, autonomous navigation, dynamic obstacle avoidance, equipment detection, and data archiving. The solution has been deployed at multiple steel enterprises including Xiangtan Iron and Steel and Jinnan Iron and Steel, has entered a phase of regular on-site operation, and is planned for reuse in scenarios such as power cable tunnels and chemical industrial parks.
Topstar's humanoid dual-arm robot AOI glass-wiping workstation uses ±0.05mm repeat positioning accuracy, combined with visual positioning and flexible force control, to achieve targeted wiping without damaging glass in optical workshops. The workstation integrates inline AOI inspection and wiping in a single unit, replacing the traditional two dedicated machines and two workstations, and relies on a high-resolution industrial camera and an edge AI computing unit to perform defect detection and classification positioning. It has now been validated in the workshops of a leading 3C customer and an optical customer; ordinary production line workers can quickly learn to operate it, helping customers improve overall workshop yield.
GenTrack3 proposes an online multi-object tracking (MOT) framework that integrates deterministic and stochastic principles, using stochastic particle sets to compute target-to-detection matching costs in order to maintain robust tracking under uncertainty. The paper also introduces a new track-to-detection matching method that improves scalability as the number of targets grows and supports group tracking; tracking inference uses tracklets containing identifiers, states, velocities, track penalties, and track ages. The authors state that a reference implementation and baseline trackers have been released on GitHub.
The authors propose TopoGS-SLAM, which decouples refinement from capacity using coverage-triggered Gaussian initialization and bounded-topology optimization, turning mapping into a bounded-dimensional problem. On standard indoor benchmarks, its localization and reconstruction accuracy is not inferior to densification-based pipelines, while memory scalability and throughput improve significantly. The authors therefore conclude that in bounded-area static indoor scenes with sufficient multi-view coverage, Gaussian SLAM does not require iterative topology mutation.
GenTrack2 proposes a visual multi-object tracking method that jointly uses stochastic particle filtering and deterministic association to maintain identifier consistency under conditions where the number of targets is unknown and changes over time and the dynamics are nonlinear. The particle filter uses particle swarm optimization (PSO) to guide particles toward the modes of the state distribution, with a fitness measure that combines motion consistency, appearance similarity, and social-interaction cues with neighboring targets; deterministic association strengthens identifier consistency through a cost matrix that incorporates spatial consistency between particles and current detections, detection confidence, and track penalties.
DepTR-MOT proposes a category-agnostic DETR-based detector that embeds instance-level depth awareness into the detector through instance-level soft depth label supervision and dense depth map distillation, and outputs instance depth at inference to improve multi-object tracking under occlusion and close-proximity interactions.
The authors propose LENS, which accelerates ACEsplat-style scene memory workloads on an AMD KR260 FPGA/ARM platform, comprising an INT16 scene coordinate regression accelerator and a fixed-point ordered 3DGS renderer.
The authors propose RMMBench, a mobile manipulation benchmark that requires robots to understand language instructions and execute long-horizon tasks in continuous space, with a "navigation-manipulation" task suite covering 70 typical task scenarios. Results show that leading vision-language models (VLMs) still face notable challenges in spatial localization for mobile manipulation tasks, and their spatial perception in long-horizon interactions needs strengthening. The work has been accepted to CoRL 2026.
The study proposes Embodied Agent Arena, evaluating ten VLM agents across five dimensions—geometry, spatial reasoning, affordance, task planning, and manipulation—with 1,000 cases, including the 168-case GeoProbe benchmark for camera, motion, depth, and scale estimation.
PartialBiGrasp is a dual-arm grasp generation framework that operates directly on partial point cloud observations. It implicitly learns geometric features through a convolutional occupancy network to reason about graspability, collision-free contact regions, and object thickness. Based on this, the method generates force-closure-compliant grasp pairs and uses sampling-based optimization to correct for ambiguity caused by incomplete geometry. The authors evaluated the method using analytical force-closure metrics, large-scale simulation experiments, and noisy partial point clouds on a real robot.
This paper has been withdrawn by its author, Zeyu Ni. OccamView is an object-conditioned view-selection framework for frame-budgeted active 3D Gaussian reconstruction. It builds an online object memory from open-vocabulary detections, represents unresolved local occupancy around detected objects as conservative hidden-region proxies, and evaluates candidate viewpoints with an occlusion-aware proxy-coverage score.
The authors propose the Language Sensor Model (LSM), which maps each natural language description and its scene-graph context to a multimodal distribution, using mixture weights to represent referential ambiguity and component covariances to represent spatial uncertainty.
This paper proposes estimating the coefficients of a compact Geometric Variable Strain (GVS) parameterization within a factor graph framework, and uses a kinematic factor derived from the Magnus expansion of the strain field to link the GVS strain coefficients with backbone pose variables, thereby reconstructing the shape of a continuum manipulator. The method is compared against a Gaussian process regression baseline on simulated and experimental data, achieving higher accuracy in partial and sparse measurement scenarios while maintaining computational efficiency. The state vector is compact and can be used directly for model-based control.
The authors propose the SAGE (Semantic-to-Action Grounded Execution) framework, which uses trajectories output by a vision-language model (VLM) as preferences over complete geometric candidate paths, and combines each candidate's geometric score with its agreement with the VLM trajectory through rank fusion.
The authors propose PnP*, which they describe as the first practical certifiable solver that guarantees efficient computation while returning a globally optimal camera pose. The paper proves that the convex relaxation is exact under moderate noise, and uses chordal sparsity patterns and quotient ring reduction to make the relaxation tractable and scalable. Numerical validation shows that PnP* returns provably correct camera poses at over 10 Hz.
The study proposes an OCT-guided data-driven framework that learns soft tissue shape control from user demonstrations and executes it with closed-loop OCT feedback. In 12 configurations and 576 phantom trials, the Explicit policy using residual-conditioned voxel point clouds achieved Shape RMSE of 0.534 mm on the depth task and 0.443 mm on the directional task; full-volume Diffusion with residual conditioning reduced directional error from 0.748 mm to 0.555 mm.
AWARE is a world-model framework that relies solely on external camera monitoring, performing anomaly detection through system dynamics estimation and state prediction without onboard sensors such as motor encoders. On 240 real-world robotic crane trajectories (including payload collisions and motor degradation), its anomaly classification improves by 26% over the strongest baseline; the inferred dynamics parameters can also be decoded to localize the source of faults, achieving 88% accuracy in localizing degrading motors.
The authors propose augmented filtered Monte Carlo localization (AF-MCL), which achieves 2D indoor UAV localization using only four single-point range sensors and measured heading, rejects range measurements inconsistent with a known static map, and adaptively injects particles to handle localization divergence and kidnapping.
The authors propose OWLS (Open-world Language-conditioned Speed Fields), which plans directly over the dense feature maps of a vision-language model (VLM): given a description of traversable terrain, the VLM scores every pixel of a single camera image, and OWLS projects these scores onto the ground, adapts them to the platform with two morphological operations, and uses them as the speed field of a minimum-time planner, so that terrain appearance and obstacle spacing jointly shape a single trajectory.
The study proposes an anatomy-anchored imitation learning framework that combines anatomy-guided probe placement with learned ultrasound navigation. Across 16 unseen participants, task success rates for standard-view acquisition of the kidney, gallbladder, and liver and for radial artery scanning were 96%, 94%, 88%, and 94%, respectively.
TravSUITE is a traversability system for high-speed off-road navigation, consisting of a voxel mapper that uses visual foundation models to build a geometric-semantic local map and a unified neural network that jointly predicts geometry, semantics, speed, and cost in bird's-eye view (BEV).
To address input bias in GP regression caused by mobile sensor pose estimation errors, a bias-aware Gaussian process (Ba-GP) framework is proposed. It uses the recorded GP posterior-mean gradient to propagate input bias and noise into mean and covariance corrections, and provides a numerically efficient first-order correction algorithm based on the additive covariance structure.
This paper investigates whole-body proximity sensors as an inductive bias for learning reflexive collision-avoidance behavior, finding that low-resolution lossy sensors were a stronger bias than high-resolution full coverage. The study compared eight candidate representations in a reinforcement learning environment, including latent compression, future contact anticipation, lossy corruption, and privileged state estimation, and trained over 200 humanoid robots end-to-end to play a simplified version of dodgeball under intentionally constrained sample and compute budgets. The authors also found that anticipating when and where contact occurs actually reduced learning sample efficiency.
FORCA is a coding agent that takes visual observations and wrist force/torque measurements as input, writes Python programs for contact probing and force-responsive motion, and between executions uses force records together with visual outcomes to diagnose failures and revise motion and force-feedback control logic.
The authors propose RepresentNav, a navigation foundation model that uses Representation Residuals, a three-layer transformer, to aggregate full-depth features from multiple vision foundation models and construct a representation space driven by the policy's own objectives.
Why it matters: The paper proposes constructing a visual representation space using the policy's own objectives and predicting future scene representations within it, offering transferable ideas for the representation design of navigation policies.
Hongxing Technology has globally launched Lumi, an embodied AI eye-care lamp robot. Lighting is only its basic function; the product is equipped with a millimeter-wave radar sensing system, a multimodal AI perception module, and nine types of memory systems, along with a 7-stage growth system and a thousand dynamic postures. Lumi is now open for global pre-order, and the company positions it as a new desk lamp category shifting from a "lighting tool" to an "emotional companion."
Bluecore Robotics will showcase five highlights at Booth C3-3, Hall N1, Shanghai New International Expo Centre, from November 3 to 6, 2026, including the VR-headset-teleoperated humanoid robot VB2, the humanoid robot chassis H6, the point-to-point transport robot V400, ultra-thin latent and cage-cart transport robots, and Mayer Vision 3D vision sensors. The V400 targets lightweight short-distance transport, requires no deployment of a scheduling system, and supports rapid deployment in 30 minutes; the ultra-thin latent robot has a body thickness of 120 mm. Visitors can experience the VB2's multimodal perception, task understanding, and precise force control capabilities on site.