Reddit user deemco posted asking for help, saying they have no idea how to build a robot from scratch and are currently stuck on hardware connections and system integration, hoping to get all the parts working together. They envision building a flying companion robot and plan to use parts such as motors, a camera, a screen, and a Raspberry Pi, and they hope to get a low-cost parts list.
An online diagnostics toolkit for drones has launched an open-source collaboration on Reddit r/robotics. Tasks are broken into numbered items, and contributors can take one on and submit a PR to participate.
Reddit user /u/lazyenergetic is asking for help designing a mounting bracket for two motors, hoping to find two mounting hole positions on the same plane and only considering a single-hole solution as a last resort. The robot's total weight (excluding motors) needs to stay under 100 pounds, and the poster has metal cutting and welding capabilities.
This article systematically explains how to choose brushless DC (BLDC) motors for robotic arms and mobile robots, emphasizing that selection should start from the mechanical load rather than the controller catalog, and covering torque calculation, commutation methods (six-step, sine, FOC), feedback architecture, communication interfaces, and safety design. The article provides a ten-step selection process, a decision table for control schemes in different application scenarios, as well as debugging verification and an engineer checklist.
An analysis aimed at robot selection points out that wrist geometry (inline wrist, offset wrist, collaborative robot layout) determines the behavioral differences in production among robots with the same payload and the same reach. In an inline wrist, joints 4, 5, and 6 intersect at a single point, supporting analytical inverse kinematics and offline programming; an offset wrist provides a larger orientation envelope for long tools (spray guns, welding guns, grinding heads), but increases the complexity of inverse kinematics and singularity analysis.
A mechatronics graduate posted on Reddit for help: their graduation project involved leading the control system for a 20+ DOF humanoid robot using ROS, Gazebo, RViz, and C++. They want to choose one direction to go deeper in: real-robot control, embedded systems, navigation and SLAM, manipulation, or reinforcement learning. They ask about suitable learning paths, which books, courses, and open-source projects are worth the time and which to skip, hands-on projects that can demonstrate ability, and common skill gaps for people with this background.
The Reddit robotics community discusses servo drive packaging options for compact robotic joints: panel-mount drives are easier to maintain and dissipate heat, but wiring the high-current motor phase lines and encoder lines for 6 joints is complex; some developers are considering abandoning a central control cabinet and instead integrating miniature PCB-mount drives such as Elmo and Synapticon directly onto custom joint carrier boards. The discussion focuses on whether the simplified wiring is worth the cost in heat dissipation and maintainability.
Using the Qinglong configuration as a case study, this work derives the construction methods for the center-of-mass model, zero-moment point constraints, receding-horizon quadratic programming, and task-priority force distribution in humanoid robot motion control. The method was validated in real physics simulation for locomotion performance in walking, speed variation, stair climbing, jumping, and disturbance-rejection scenarios.
RobotMCP is aimed at letting general-purpose large models discover, subscribe to, and call an existing ROS/ROS2 system without modifying the robot's source code. The core conflict it addresses is that, in the past, connecting large models to robots often required changing code on the robot side. The original article breaks down RobotMCP's dependency chain and protocol layers.
RoboTTT targets a structural flaw in robot foundation models: mainstream policies such as VLA struggle to remember things that happened earlier. It proposes using fast weights and context scaling to improve long-horizon memory. The original text is an excerpt from an introduction and does not provide full experimental details.
Niantic Spatial and Flexion have jointly launched a simulation data collection solution based on 3D Gaussian Splatting, aimed at alleviating the contradiction in humanoid robot training: millions of interactions are needed, yet the physical world does not allow repeated trial and error. The solution reconstructs real scenes via 3D Gaussian Splatting, providing a simulation data source that allows repeated trial and error for robot learning.
Starting from the instability mechanism of a rolling inverted pendulum, the article explains why two-wheeled-legged robots must rely on wheel rolling to maintain balance, and presents the complete mathematical formulation of proximal policy optimization (PPO). Using a real training experiment of three million steps, the author shows the policy's progression from random jitter to standing in place and moving toward a target.
Based on the MJX parallel reinforcement learning stack in MuJoCo Playground, the four-fingered dexterous in-hand reorientation task was reproduced, using official ONNX weights to deploy the C engine and complete eight rounds of real inference along with failure boundary analysis.
A complete reproduction of the Action Chunking Transformer (ACT), implemented with LeRobot, on the MuJoCo dual-arm block handover task, covering the CVAE and action-chunking derivations, frame-by-frame comparison against the dataset to validate the inference pipeline, and a 20-episode deterministic closed-loop evaluation with a comparison of execution modes.
The SONIC method performs deterministic closed-loop runs in MuJoCo simulation on released checkpoints for the G1 humanoid robot, reproducing general control with whole-body motion tracking as the base task. The method unifies motion representation, adopts a tokenized conditional policy and Gaussian kernel rewards, and presents real footage and error metrics for multiple skill entries.
This article runs the Upkie wheeled biped robot's control system entirely in simulation, starting from the wheeled inverted pendulum model and constrained predictive control to explain the hierarchical structure of balancing and steering, and covering scenarios such as standing in place, acceleration and deceleration, differential steering, push recovery, and instability boundaries.
The RB-Y1 wheeled dual-arm robot demonstrates redundant whole-body control based on generalized coordinates and the Jacobian, including null-space utilization for manipulability maximization and joint-limit avoidance. The experiments show push-disturbance recovery under task-space impedance control, as well as mobile manipulation in which base movement expands the workspace.
This article walks through the hands-on process of developing a custom RViz2 display plugin for ROS2. Aimed at robotics developers and newcomers, it covers the steps for developing an RViz2 plugin and helps readers learn how to extend RViz2's visualization capabilities in a ROS2 environment.
m-explore-ros2 provides an autonomous exploration mapping solution under ROS2, using frontier exploration strategies to drive robots to move autonomously and build maps in unknown environments. Aimed at ROS2 developers, the solution can be used for hands-on autonomous exploration and mapping with mobile robots.
This practical guide to ROS2 custom message/service/action interface development explains how to write custom interfaces from .msg and .srv to .action. It covers how to define and use the three interface types—messages, services, and actions—and is aimed at robotics developers and newcomers.
This article explains how to use ros2_tracing and LTTng to profile and trace ROS2. ros2_tracing is ROS2's official tracing tool, built on LTTng; it can record runtime events such as nodes and topics, and is used to locate latency and performance bottlenecks.
This article is a hands-on Zephyr tutorial for robotics developers, explaining how to use Zephyr's PWM and LED subsystems to implement LED dimming, LED strip control, and breathing effects. It covers driver configuration and usage for the PWM subsystem and the LED subsystem, including PWM LEDs and WS2812 LED strips.
This article explains practical usage of I2S, DMIC, and audio data streams in the Zephyr audio subsystem. It covers the Zephyr RTOS audio peripheral interfaces and data stream processing, aimed at robotics and embedded developers to help them understand how microphone capture and audio transmission links are implemented.
This article explains the Zephyr real-time operating system (RTOS) testing system in practice, covering the Ztest testing framework, the Twister testing tool, and how they work with continuous integration (CI). Aimed at robotics developers, it describes how to set up automated testing and integration workflows in Zephyr projects.
This article explains practical methods for dual-core scheduling and inter-core communication in the Zephyr real-time operating system under multicore and SMP (symmetric multiprocessing) scenarios. Aimed at robotics developers, it covers task allocation and inter-core data exchange mechanisms for Zephyr on multicore processors, helping readers understand how to use multiple cores to improve real-time performance in embedded control.
This article explains practical key points for CAN bus development on the Zephyr RTOS, covering the configuration and use of CAN controllers, how to set up CAN filters, and the implementation of the ISO-TP (ISO 15765-2) transport protocol, aimed at robotics and embedded developers.
Zephyr secure boot and OTA updates center on MCUboot, image slots, and rollback mechanisms, used to achieve secure boot and remote firmware updates on MCUs. The image slot design supports writing new firmware and rolling back on failure, reducing the risk of a device being bricked by a failed update.
A practical walkthrough of the Zephyr sensor subsystem, covering how to use the Sensor API, the trigger mechanism, and data flow. Aimed at robotics developers, it covers calling sensor driver interfaces, configuring triggers, and the paths along which data moves through the subsystem.
A project plan for using drones equipped with RK3588 for ship detection and on-site verification in shipyards, covering image capture, model training, on-board inference, and evidence preservation. The plan includes five scenario images showing detection boxes and explains occlusion handling and the deployment verification process.
Using a quadrotor drone as its subject, the article explains the relationship between thrust, gravity, and attitude changes, and builds a six-degree-of-freedom flight dynamics model. The author combines 3D model diagrams and simulation code to demonstrate the position tracking process, helping readers understand the modeling approach and how to run it.
The "3D Vision from Beginner to Mastery" Knowledge Planet has been running for nearly 9 years and now has more than 6,400 3D vision practitioners. It includes nearly 20 core video course tracks, covering industrial 3D vision, SLAM, 3DGS, autonomous driving, drones, embodied intelligence, and other directions.
On an RDK X5 V1.0, reading from both I²C sensors, the VEML7700 (address 0x10) and the VL53L8CX (address 0x29), failed, with address NACK, a timeout of about 1.06 seconds, and a Remote I/O error. The same two modules read successfully on an ESP32-C3 at 100kHz, and the VL53L8CX also output 8×8 distance data at about 2Hz.
In machine vision, CNNs and Transformers divide work according to real-time performance and complexity: CNNs handle lane recognition, close-range obstacle detection, traffic sign classification, and workpiece positioning and defect detection for industrial robots, while Transformers handle bird's-eye-view scene reconstruction, multi-sensor fusion, trajectory prediction, and drivable-area segmentation.
In a training platform built on a self-made MuJoCo physics simulator, the developer trained a quadruped robot dog simulation using reinforcement learning with motion imitation (style reference motions). Trained for only 2 hours on a MacBook Air CPU (no GPU), it learned a keyframe trot gait and balance. Joints are limited by a realistic hobby servo model in torque, speed curve, backlash, and latency, making the gait smoother than pure reinforcement learning. The simulation supports WASD-driven multidirectional control.
Meccano M.A.X. has made progress on a new tool combining Mr. Dreambot's MeccaChannel, ESP32, Arduino IDE, and ChatGPT, with a library usable with only minor modifications delivering operating results for the gripper and multiple servos.
SLAM is beginning to use contact information between the robot and the environment as a basis for localization, supplementing pose estimation with kinematics and foot-ground constraints when vision and LiDAR are limited. KD-RIEKF from IROS 2025 was validated on the Unitree Go2, and under its test conditions, both position and attitude estimation errors decreased when the robot moved at high speed.
A developer on Reddit demonstrated driving a Husky A300 mobile robot with a local Qwen3 LLM agent through ROS2 Nav2: it uses an asynchronous background task architecture, so chat still responds during navigation, and llama.cpp was compiled with CUDA 12.4 for real-time local inference with no cloud dependency. Spatial coordinates are kept in the conversation context with no external database; it currently runs entirely in simulation, and the same action-client/function-calling architecture can be transferred directly to a physical robot.
Xbotics and Hangzhou Robotics School jointly present the 'Embodied Frontier Half-Day Offline Course,' held on October 25, 2026, 13:00–17:00 on the 3rd floor of Hangzhou Robotics School, at 400 yuan per person, limited to a small class of 15.
If the RDK X5 shows an inaccurate time after rebooting without a network connection, you can connect a 2–3.3V rechargeable battery to the onboard RTC battery connector (connector 2) to keep time. After setting the time with date -s, write it to the RTC with hwclock -w, then add hwclock -s to startup so the system time is automatically written back.
The D-Robotics community has released the RoboGo image rdk-s-lerobot-act-pi0-s600-cu128-v1.0, which consolidates the ACT and π0 development environments into a unified web workbench for deployment on the RDK S600.