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#Robots (24)

WaterfallPagination
RoboSim: A Robot-Native Rust Simulation Engine

RoboSim: A Robot-Native Rust Simulation Engine

@arai01robo · 102 views · 2026-09-09
SimulationRustRobotics
AGIBOT Open-Sources Its World 2026 RL Dataset
0:28

AGIBOT Open-Sources Its World 2026 RL Dataset

@spaceandtech_ · 244 views · 2026-09-03
AgiBotEmbodied AIReinforcement Learning
Humanoid robot fills popcorn boxes for cinema service
0:42

Humanoid robot fills popcorn boxes for cinema service

@CyberRobooo · 196 views · 2026-09-02
CyberRoboooHumanoidService robots
Hydra-0 world model learns actions as pixel motion
0:05

Hydra-0 world model learns actions as pixel motion

@techniahqrobot · 259 views · 2026-09-02
Hydra-0NVIDIAVLA
Agility co-founder: humanoid robotics needs a reality check
0:50

Agility co-founder: humanoid robotics needs a reality check

@agilityrobotics · 205 views · 2026-08-31
Agility RoboticsAgilityDigit
Watney robot folds laundry around the clock
0:39

Watney robot folds laundry around the clock

@ctorobotics · 230 views · 2026-08-31
Watney RoboticsWatneylaundry folding
Anthropic Opens the Model Hardware Standard Research Preview: A Shared Interface for AI Agents to Operate Physical Equipment

Anthropic Opens the Model Hardware Standard Research Preview: A Shared Interface for AI Agents to Operate Physical Equipment

MHS uses standardized drivers, read/write primitives, natural-language machine descriptions, and MCP/CLI/API control to connect heterogeneous lab and manufacturing equipment to AI agents. The preview includes AWS, Doosan, Universal Robots, Tecan, QIAGEN and others, while emphasizing expert oversight for physical reasoning and safety.

BLOG

MHSMCPAI AgentPhysical DeviceHardware Interface
Jarvis Autonomously Explores Room via AgenticROS + Natural Language
0:47

Jarvis Autonomously Explores Room via AgenticROS + Natural Language

@chrismatthieu · 57 views · 2026-08-24
Agentic AIAgenticROSROS2
BWsensing IMU400C High-Precision Inertial Measurement Unit

BWsensing IMU400C High-Precision Inertial Measurement Unit

BWsensing (Wuxi) IMU400C is a high-precision 6-axis inertial measurement unit: gyro ±400 °/s with 0.01 °/s resolution, bias stability <0.5 °/h (100 s, 1σ) and angular random walk <0.1 °/√h; accelerometer ±3.6 g with 0.01 mg resolution and 0.001 mg bias stability. 500 Hz output over RS232/RS485/TTL, measuring 60×59×29 mm with −40 to +85 °C operation. Vendor-listed applications include unmanned boats/underwater robots, AGVs, robots and drones.

COMPONENT

IMUHigh PrecisionBWsensingBeijing WeisensorUnmanned Boat
SuperMap Turns SLAM into Persistent 4D Spatial Memory
3:52

SuperMap Turns SLAM into Persistent 4D Spatial Memory

@robotsdigest · 121 views · 2026-08-16
SLAMSuperMapSpatial memory
Movella (Xsens) MTi-2 Micro Vertical Reference Unit (VRU)

Movella (Xsens) MTi-2 Micro Vertical Reference Unit (VRU)

The Movella (Xsens) MTi-2 is a micro VRU in the same 0.6 g footprint as the MTi-1, adding drift-free roll/pitch output on top of calibrated IMU data, with 2000 deg/s gyro range for gimbals, robots and antenna alignment.

COMPONENT

VRUAttitude referenceMEMSInertial measurementGimbal Stabilization
Movella (Xsens) MTi-1 Micro IMU Module

Movella (Xsens) MTi-1 Micro IMU Module

The Movella (Xsens) MTi-1 is an ultra-compact self-contained IMU (12.1x12.1x2.55 mm, 0.6 g) delivering calibrated 3D acceleration, rate of turn and magnetic field with 6 deg/h bias stability and strapdown integration for UAVs and robots.

COMPONENT

IMUInertial measurementMEMSMiniatureDrones
Movella Xsens MTi-620R Rugged VRU

Movella Xsens MTi-620R Rugged VRU

Movella (formerly Xsens) MTi-620R is a rugged industrial VRU: IP68 aluminum housing at 75 g, 0.2 deg RMS roll/pitch, 2000 deg/s gyro with 8 deg/h bias stability and 0.007 deg/s/sqrt(Hz) noise; onboard magnetometer and barometer, 4.5-24 V at <0.5 W, CAN/RS232, up to 2 kHz. Reliable roll/pitch for inspection robots, unmanned platforms and marine systems.

COMPONENT

IMUVRUIP68RuggedInspection
Movella Xsens MTi-610R Rugged Industrial IMU

Movella Xsens MTi-610R Rugged Industrial IMU

Movella (formerly Xsens) MTi-610R is a rugged industrial IMU: IP68 aluminum housing at 75 g, 2000 deg/s gyro with 8 deg/h bias stability and 0.007 deg/s/sqrt(Hz) noise, 60 ug/sqrt(Hz) accelerometer noise; onboard magnetometer and barometer, 4.5-24 V input at <0.5 W, CAN/RS232, up to 2 kHz output. Reliable inertial measurement for inspection robots, unmanned platforms and off-road vehicles.

COMPONENT

IMUInertial measurementIP68RuggedInspection
Movella Xsens MTi-G-710 GNSS/INS

Movella Xsens MTi-G-710 GNSS/INS

Movella (formerly Xsens) MTi-G-710 is a GNSS/INS module with u-blox MAX-M8: 1.0 m (1 sigma) position, 0.05 m/s velocity, 0.2 deg RMS roll/pitch, 0.8 deg RMS heading; 450 deg/s gyro with 10 deg/h bias stability, IP67, 58 g, 660 mW, USB/RS232/RS422. Dependable combined navigation for inspection robots, ag machinery and vehicles.

COMPONENT

IMUGNSSINSIntegrated navigationInspection
Movella Xsens MTi-630R Rugged AHRS

Movella Xsens MTi-630R Rugged AHRS

Movella (formerly Xsens) MTi-630R is a rugged industrial AHRS: 2000 deg/s gyro with 8 deg/h bias stability, 0.007 deg/s/sqrt(Hz) noise, 0.2 deg RMS roll/pitch and 1 deg RMS heading; IP68, 75 g, <0.5 W, 4.5-24 V input, CAN/RS232, up to 2 kHz output. Reliable attitude and heading for inspection robots, unmanned platforms and marine systems.

COMPONENT

IMUAHRSInertial navigationIP68Inspection
Ouster OS0 Ultra-Wide View High-Resolution Imaging Lidar

Ouster OS0 Ultra-Wide View High-Resolution Imaging Lidar

The Ouster OS0 is the Rev8 ultra-wide short-range imaging lidar: 90 deg vertical FOV (+45 to -45) and 360 deg horizontal, 32/64/128 channels, 512-4096 horizontal columns (0.088 deg angular resolution at 4096) and 5-40 Hz configurable rotation. In 1024 @ 10 Hz mode it reaches 75 m on 80% Lambertian targets and 35 m on 10% targets, both at >90% detection probability under 100 klx sunlight, with 500 m max representable range and a minimum range configurable down to 0 m (0.5 m default, 0.3 m optional). Range accuracy is +/-1.25 cm (Lambertian) / +/-2.5 cm (retroreflective) - 2x better precision and accuracy than Rev7 - at 0.1 cm range resolution, with up to 2 returns and 10,485,760 points per second; the 865 nm laser is Class 1 eye-safe. Native RGB-D color point cloud (116 dB dynamic range) carries RGB, range, signal, reflectivity, NIR, channel, azimuth and timestamp per point, with a synchronous IMU at 640/1280/2560 Hz. Data leaves over gigabit Ethernet UDP with PTP/gPTP/NMEA/PPS time sync (<1 ms error) and <10 ms latency. It runs from 12/24 VDC (9-58 V) at 10-20 W, measures 87 mm diameter x 58.35 mm and weighs 500 g (580 g with halo cap); IP68/IP69K, -40 to +85 C (Rev7 was +60 C), 100 g shock, 10 Grms vibration and MTTF over 250,000 h. Engineered for functional safety (ASIL-B, SIL-2, PLd) with an on-sensor 3D Zone Monitor and designed for cybersecurity to ISO 21434 / UNECE WP.29 / IEC 62443 / ISO 27001. Built for AMRs, AGVs, inspection robots, heavy machinery, drones and mapping solutions that need close-range blind-spot perception and high-accuracy 3D modeling.

COMPONENT

LiDAROusterRGB-DUltra-Wide AngleShort Distance
Movella Xsens MTi-300 (Sirius) Rugged AHRS

Movella Xsens MTi-300 (Sirius) Rugged AHRS

Movella (formerly Xsens) MTi-300 belongs to the rugged Sirius family: +/-300 deg/s gyro with 7 deg/h in-run bias stability, 400 Hz bandwidth, 0.003 deg/s/sqrt(Hz) noise density, +/-8 g accelerometer with 15 ug bias stability; IP68 and MIL-STD shock/vibration rated, -40 to +85 C, aluminum housing with ODU connector at 78.5 g. Reliable roll/pitch/heading for inspection robots, USVs and autonomous systems.

COMPONENT

IMUAHRSInertial navigationPoseInspection
RoboSense AC1 Active Stereo Depth Camera

RoboSense AC1 Active Stereo Depth Camera

RoboSense AC1 is an active stereo depth camera for robots and outdoor use: 70 m max range, 100 klux sunlight resistance, 1 cm (1 sigma) depth accuracy, 120x60 deg depth FOV, 144x78 deg RGB FOV, -20 to +60 C operating range; stable 3D perception in bright sunlight for inspection robots, AGVs and UGVs, combinable with LiDAR for multi-sensor fusion.

COMPONENT

Depth camerasStereo vision3D PerceptionInspectionRobots
Gemma powers Reachy Mini offline robot voice assistant
0:45

Gemma powers Reachy Mini offline robot voice assistant

@googlegemma · 55 views · 2026-08-12
GemmaReachy MiniRaspberry Pi 5
RNoidHumanoid

RNoid

robot

HumanoidRobots
SILICA: Repurposing Diffusion Priors for Joint Glass Segmentation and Depth Estimation

SILICA: Repurposing Diffusion Priors for Joint Glass Segmentation and Depth Estimation

Standard depth sensors systematically fail on transparent surfaces, creating corrupted 3D maps and severe navigation hazards. While specialized hardware sensors can detect glass, they lack modularity and have extensive hardware dependencies. Consequently, learning-based monocular depth estimation has emerged as a compelling alternative. However, domain-specific glass-aware monocular depth estimators struggle with unfamiliar indoor layouts; restricted by the severe scarcity of real-world glass depth annotations, they fail to generalize zero-shot to new settings. This motivates us to explore whether the extensive priors of text-to-image diffusion models can enable generalizable perception of transparent surfaces. We introduce SILICA, a unified pipeline leveraging these priors to jointly predict glass segmentation and glass-aware depth. This mutual information exchange establishes a robust spatial hierarchy, entirely eliminating the need for paired real-world glass depth annotations. Subsequently, we use the predicted segmentation mask to explicitly filter incorrect glass depth points from standard sensors, recovering accurate metric glass depth for downstream 3D mapping and autonomous collision avoidance. Supported by our novel Mirage 18k dataset, extensive experiments demonstrate that SILICA achieves remarkable zero-shot transfer across diverse, unseen environments, outperforming state-of-the-art models by almost 20% and setting a new benchmark for transparent surface perception.

Tarun R, Anuj Verma, Laksh NanwaniJul 27, 2026
RoboticsRobotsJul 27, 2026
Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors

Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors

This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking model predictive control formulation that enforces corridor feasibility and dynamic consistency within a single optimization problem. To guarantee convergence to the target and mitigate the occurrence of local minima induced by non-convex geometry, a shortest-path-based offset cost with feasibility constraints is embedded directly into the planning problem. Numerical simulations show that the proposed formulation generates dynamically valid trajectories that satisfy the corridor constraints and converge to the target without relying on external global planning stages.

Henrique Silva, Marcelo A. Santos, Guilherme V. RaffoJul 27, 2026
RoboticsRobotsJul 27, 2026
Accuracy potential of visual localization exploiting high-end street-level imagery

Accuracy potential of visual localization exploiting high-end street-level imagery

Accurate and reliable pose information with respect to a reference frame is increasingly demanded across applications such as autonomous navigation, surveying, robotics, and augmented and mixed reality. Visual localization can serve as a complementary positioning modality to GNSS, whose applicability and accuracy are often limited. Yet, the accuracy potential of visual localization has not been systematically investigated against survey-grade demands. This is mainly due to the lack of publicly available, large-scale outdoor datasets with ground-truth poses in the sub-centimeter range. In this work, we address both gaps. We introduce a scalable visual localization pipeline that employs precisely georeferenced, high-resolution street-level imagery directly as the scene representation. It combines prior-guided reference candidate selection with on-the-fly local Structure-from-Motion reconstruction and PnP-based pose estimation. We further present the FHNW Muttenz dataset, a real-world dataset covering a contiguous 10 km street network mapped in two mobile mapping campaigns approximately 1.5 years apart. It consists of high-resolution reference imagery and query sequences acquired by four different cameras across five representative scenes. All images are precisely co-registered, yielding 6-DoF ground-truth poses in the sub-centimeter range. Using this dataset, we evaluate the accuracy potential of visual localization. Our experiments demonstrate median pose accuracies in the range of 1-5 cm for translation and 0.05-0.1° for rotation, reaching as low as 1 cm and 0.03° under favorable conditions. These results show that visual localization can complement survey-grade GNSS positioning, paving the way for 3D geospatial data acquisition using consumer devices and fully automated georeferencing approaches. The dataset is publicly available at: https://fhnw-muttenz-vl-dataset.github.io/.

Jonas Meyer, Stephan Nebiker, Pascal TheilerJul 27, 2026
RoboticsRobotsJul 27, 2026