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#Guides and practice
Oct 30Fri
  1. Reddit r/robotics42

    Mino's ROS 2-based companion behavior explained: three states of finding, following, and accompanying

    Mino's companion behavior application is implemented on the Jetson Orin Nano based on ROS 2, with three states: finding a person, following, and accompanying; the video is its first outdoor test. The application uses open-source modules such as depth estimation, YOLO-World person detection and tracking, pose feedback, odometry, and motion control, and a coding agent assembled the behavior logic within a weekend. The robot perceives the person by 3D position and velocity, smooths the trajectory with a Kalman filter, and continuously sends head pose and chassis velocity commands to custom hardware.

  2. 锐曼REEMAN35

    How does the REEMAN YIMA delivery robot use elevators in apartment buildings? YBOT20P explained

    The REEMAN YIMA YBOT20P outdoor delivery robot can automatically call an elevator, enter it, and deliver across floors through a compatible elevator control interface, without anyone needing to press the elevator buttons. The platform is designed for indoor and outdoor transport, has a payload of 100 kg, uses dual-lidar SLAM navigation, autonomous obstacle avoidance, and automatic charging, and can connect the delivery process from a community parcel station to a designated apartment entrance. Elevator integration requires confirming compatibility with the manufacturer's API, control module, or building system interface, and the elevator's existing safety controls must not be bypassed.

  3. 古月居 · 微信公众号58

    Open X-Embodiment and RT-X: A Practical Guide to Cross-Embodiment Transfer for Large-Model Robot Policies

    This article provides a systematic walkthrough of cross-embodiment transfer practice with Open X-Embodiment and RT-X, covering RLDS data-format unification, the RT-X model architecture, pretraining, fine-tuning, and deployment on new robots. It presents RLDS conversion scripts, a PyTorch implementation of an RT-1-based model, and training techniques such as temperature sampling and embodiment-weighted loss, and it documents six categories of pitfalls, including slow data loading, poor cross-embodiment performance, and action-space mismatch, along with corresponding solutions.

  4. Reddit r/robotics36

    Local LLM converts English commands into general robotic arm control: Qwen3.8-Flash-Next drives reBot B601 demo

    A developer used a locally running Qwen3.8-Flash-Next model to convert English commands into general control of a reBot B601 robotic arm, entirely offline at home. In the demo, the arm completed tasks including shooting a Nerf gun, throwing a ball, writing, drawing, and pouring a cup, with object recognition using Meta SAM. This is the second video in the series; an earlier one demonstrated give, take, follow, and stacking actions.

  5. Reddit r/robotics78

    PrismaX shares π0.5 policy transfer across YAM devices: fine-tuning ablations, latency bug, and 16GB VRAM deployment

    The PrismaX team shared its practice of transferring a π0.5 policy trained on one YAM device to another YAM device, covering three parts: fine-tuning ablations, latency troubleshooting, and VRAM compression. On fine-tuning, training only the action expert yielded a 51% grasp rate and a 12% drop rate; full fine-tuning gave 89% and 53%; r=128 LoRA gave 84% and 59% while using 32GB. Strong lighting roughly halved the performance of the checkpoint trained only on the action expert.

    Why it matters: The original post provides details on fine-tuning ablations, latency troubleshooting, and VRAM compression for π0.5 policy cross-device transfer, which can be reused on existing equipment in small labs.

Oct 29Thu
  1. Pollen Robotics · YouTube20

    How to install the NFC Accessories Kit in Reachy Mini Wireless

    Pollen Robotics released a tutorial demonstrating how to install the NFC Accessories Kit in the head of Reachy Mini Wireless, enabling magnetic accessory detection with just four screws and no glue. Once installed, the robot can automatically recognize NFC accessories placed on its head, and each accessory can unlock a different personality in the Reachy Mini conversation app or trigger custom actions. The kit also includes extra NFC tags and magnets for users to create their own accessories and AI interactions.

  2. Reddit r/robotics58

    Implementing IMU and image-synchronized visual-inertial odometry with the OpenMV AE3

    The author used a Python script and modified firmware to synchronize the OpenMV AE3 camera's exposure with specific IMU measurements, aiming to improve visual-inertial odometry. The firmware change adds a shared clock between the two CPU cores; the IMU runs at 208 Hz, and every 10 IMU measurements trigger one camera exposure, at about 20 FPS. The code is open-sourced, and test videos of Kimera-VIO in 3 m/s outdoor flight and indoor flight are provided.

  3. Guyuehome40

    DINOv3 features plus vector database: a retraining-free approach for fixed-camera scenarios

    DINOv3 features plus a vector database can achieve retraining-free operation in fixed-camera scenarios. Mechanistically, this works by changing the data rather than the model; in terms of cost, it shortens the cycle and carries no regression risk. The approach has clear boundaries of applicability: fixed viewpoint, a limited number of categories, stable lighting, and available operations and maintenance conditions.

  4. Guyuehome62

    Analysis of Inverted-Pendulum Dynamic Balance Control for a Wheel-Legged Quadruped Robot

    The article derives the dynamics of a wheel-legged inverted pendulum and explains why the wheels must accelerate in the direction of the fall for a quadruped robot to stand on only its two rear wheels. It also analyzes the privileged-information design of an asymmetric actor-critic and tests the limits of four policies through cross-physics-engine experiments.

  5. Guyuehome58

    OpenWAM code walkthrough: Dual-System architecture and Flow Matching training pipeline

    Gu Yue Ju published a source-code walkthrough of OpenWAM, covering the project structure, the Dual-System architecture, Video/Action joint attention, the Flow Matching training pipeline, and inference deployment, organized around key code. The material provides only an abstract and does not give specific code details or conclusions.

  6. Guyuehome42

    CKT-WAM source code walkthrough: Teacher intermediate-layer feature extraction and AdapterBank routing

    This CKT-WAM source code walkthrough focuses on Teacher intermediate-layer feature extraction, AdapterBank compression and routing, and the full pipeline for injecting Context into the Student WAM. The article breaks down this information pathway from teacher features to the student world model (WAM) section by section, together with the key code files.

  7. Guyuehome51

    Robomimic Recurrent Behavior Cloning Policy: Likelihood Modeling, LSTM Deployment, and OSC Control Reproduction

    This content describes reproducing the lift and can manipulation tasks in MuJoCo using the official recurrent behavior cloning weights from the robomimic model zoo. It reconstructs the mathematical details of BC likelihood modeling, LSTM deployment semantics, and OSC impedance control.

  8. Guyuehome58

    Deploying the CLIP vision encoder to the RK3588 NPU: notes on export, RKNN conversion, and mixed quantization

    The author documents the full process of deploying the CLIP vision encoder to the RK3588 NPU, covering model export, conversion to RKNN, and mixed quantization, and collects the problems encountered along the way. The material provides only a summary and does not give specific quantization configurations or performance data.

  13. Guyuehome57

    WaterSpiderbot: Kinematic Modeling and Reinforcement Learning Gait Control for an Eight-Legged Bionic Spider Robot

    This material analyzes the eight-legged bionic robot WaterSpiderbot, covering forward and inverse kinematics of the three-joint leg chain, MuJoCo contact support mechanics, and the SAC gait policy, and provides a complete reproducible training loop. The article also diagnoses the structural reason why learning never starts under the default configuration and offers a fix.

  15. Guyuehome58

    Dexterous Hand in Practice 02: Brushless, Coreless, and Servo Motors — How to Choose Joint Motors

    Gu Yue Ju has published the second installment of its Dexterous Hand in Practice series, comparing the trade-offs among brushless motors, coreless motors, and servo motors for dexterous-hand joint selection. Currently only the title and abstract are available; the body text does not provide specific parameters or selection conclusions.

  16. Guyuehome48

    Dexterous Hand in Practice 04: Designing a Single Finger in SolidWorks, Reserving Space for Transmission and Sensing First

    The fourth installment of the Dexterous Hand in Practice series explains how to design a single-finger structure in SolidWorks. The core approach is to reserve mounting space for transmission and sensing components during the modeling stage. The content is aimed at beginners in dexterous hand R&D and is part of the structural modeling step in the design workflow.

  18. Guyuehome40

    A* and Dijkstra show no measurable difference across 18 runs in Gazebo mecanum-wheel robot simulation

    In a Gazebo mecanum-wheel robot simulation, A* and Dijkstra showed no measurable difference after 18 runs each, and the author wrote "no difference" into the conclusion. The article also discusses how to determine that an experiment showing no measurable difference is not simply the result of a flawed setup.

  19. Guyuehome60

    Minimum Snap: Differential-Flat Trajectory Generation and Geometric Control for Quadrotors

    An analysis of the minimum-snap quadrotor trajectory pipeline: piecewise ninth-order polynomials (ten coefficients) are solved in closed form with snap as the cost and derivative-continuity constraints, while COBYLA trades off time against smoothness. The differential-flat mapping yields thrust and attitude, and geometric control is validated in closed loop in MuJoCo.

  20. Guyuehome42

    Teaching GPT-6 Astra to Improve Robotic Arms from Failure: An Auditable Self-Supervised Route

    As an embodied policy candidate, GPT-6 Astra can already organize complex tasks across vision, language, state, and tool interfaces, but completing a single operation and forming new capabilities from continuous failure are problems at two different levels. The article confines self-supervised learning to this improvement stage and proposes an auditable self-supervised route for letting the model learn to improve from robotic arm failures.

  21. Reddit r/robotics22

    ESP32-S3 desktop robot Pamk full build log: from custom PCB to 3 magnetic shells

    The creator published a 20-minute full build log of the ESP32-S3 desktop robot Pamk, covering the process from changing the initial sketch to a cube shape and designing a custom PCB costing about 25 euros (integrating ESP32-S3, I2S microphone and amplifier, screen, USB-C) to assembly and 3 magnetic shells. Pamk currently has no wheels or actuators, and the author says V2 plans to add motors/actuators. The video has French voiceover and English subtitles.

Oct 28Wed
  3. Guyuehome18

    Zephyr Device Tree and Device Driver Model: A Practical Guide from DT Binding and Driver Registration to Device Retrieval

    A practical guide to Zephyr's device tree and device driver model, following the complete path from DT binding and driver registration to device retrieval. It covers the key steps from device tree description to driver registration and runtime device retrieval, aimed at developers who need to understand how Zephyr's driver model is implemented in practice.

  7. Guyuehome40

    Dexterous Hands in Practice 01: Why One Hand Is Expensive — Breaking Down Mechanical, Drive, and Sensor Configurations

    The reasons for the high cost of dexterous hands can be broken down into three aspects: mechanical structure, drive scheme, and sensor configuration. From a practical perspective, the article analyzes how these three types of configuration affect the design and price of dexterous hands, helping readers understand their cost composition.

  8. Guyuehome58

    How Much Does TEB Differ from DWA? 18 Gazebo Mecanum-Wheel Robot Simulations Compared

    The author ran 18 experiments comparing the TEB and DWA planners in a Gazebo mecanum-wheel robot simulation, with the title stating a 1.65x difference between them. The author also notes that when the planners moved sideways throughout, their lateral-movement rates differed by 100x, yet this was completely invisible on the trajectory plots.

  9. Reddit r/robotics30

    Training Franka Panda to complete pick-and-place with PPO in PyBullet

    A simulated Franka Panda learns in PyBullet to pick up blocks and place them on a target pad using PPO from Stable-Baselines3; the project aims to understand PyBullet and the full reinforcement-learning robot training pipeline. Future plans include adding text or voice prompts so users can choose which block to pick up and where to place it; the code has been released on GitHub.

  10. Reddit r/humanoidrobotics24

    Building an expressive robot head prototype, Ecko, with a Galaxy S10 and a RealMan robotic arm

    A developer built an expressive head prototype, Ecko, using a Galaxy S10 paired with a RealMan robotic arm; it is not a complete humanoid robot. The phone provides animated eyes, a camera, a microphone, and a speaker, with ARCore facial landmarks drawn inside the eyes. A Mac backend handles arm movement and Gemini-driven conversation, and displays real-time joint telemetry in a digital twin. Face tracking works within a limited range; the first motion stopped after about seven seconds due to a sign error, and that failure was kept in the build video.

Oct 27Tue
  2. Guyuehome58

    Building a ROS LiDAR Map with an RK3588 Edge Computing Box: From Wiring to Saving the Map

    This article presents the complete process of deploying ROS LiDAR mapping on an RK3588 edge computing box, covering hardware wiring, system configuration, driver debugging, indoor data collection, and map saving. It also shares troubleshooting methods for common issues, aimed at helping beginners get started.