These working notes by Russ Tedrake cover nonlinear dynamics and control with a specific focus on mechanical systems. The material explores how to achieve robust, efficient, and graceful robot movement through the integration of mechanical design, passive dynamics, and nonlinear control synthesis. Rather than relying solely on model-free approaches, the text emphasizes using the underlying structure of dynamical equations to develop more data-efficient and robust algorithms via optimization and machine learning.
Main topics include:
* Model systems such as pendulums, acrobots, cart-poles, and quadrotors
* Simple models of walking and running dynamics
* Nonlinear planning and control using trajectory optimization and LQR
* Lyapunov analysis for stability and reachability
* Estimation techniques including Kalman filters and Bayesian methods
* Learning-based approaches such as imitation learning, policy search, and system identification
* Contact-implicit trajectory optimization and hybrid systems
Main topics include:
* Model systems such as pendulums, acrobots, cart-poles, and quadrotors
* Simple models of walking and running dynamics
* Nonlinear planning and control using trajectory optimization and LQR
* Lyapunov analysis for stability and reachability
* Estimation techniques including Kalman filters and Bayesian methods
* Learning-based approaches such as imitation learning, policy search, and system identification
* Contact-implicit trajectory optimization and hybrid systems
NocKinematics is a modern, modular, and lightweight C++ Inverse Kinematics library designed specifically for Arduino and ESP32 microcontrollers. It utilizes the FABRIK (Forward And Backward Reaching Inverse Kinematics) algorithm to provide fast, iterative computations that are more efficient than traditional Jacobian Matrix approaches. The library is optimized for memory-constrained systems like AVR and ESP8266 by using specialized dynamic memory allocation to prevent RAM fragmentation.
Key features and topics:
* N-Joint Support for arbitrary numbers of connected joints.
* Memory-optimized architecture avoiding heavy std::vector usage.
* Platform agnostic compatibility with Arduino Uno, Nano, Mega, ESP8266, and ESP32.
* Practical implementation examples ranging from basic logic verification to multi-DOF servo motor control.
* Support for complex mechanisms like snake or tentacle simulations via the MultiJointSnake example.
Key features and topics:
* N-Joint Support for arbitrary numbers of connected joints.
* Memory-optimized architecture avoiding heavy std::vector usage.
* Platform agnostic compatibility with Arduino Uno, Nano, Mega, ESP8266, and ESP32.
* Practical implementation examples ranging from basic logic verification to multi-DOF servo motor control.
* Support for complex mechanisms like snake or tentacle simulations via the MultiJointSnake example.
An introduction to JD Robotics and the Stewy V2 platform, designed as an approachable learning tool for robotics. The project emphasizes open-source development, simple yet capable hardware designs that maximize motor utility, and active community support for troubleshooting and customization.
Driven by labor shortages, Japan is leading the "Physical AI" sector by integrating AI with its advanced mechatronics and hardware expertise. Supported by significant government funding, the nation is moving from experimental trials to practical deployments in logistics, manufacturing, and defense, aiming for global market dominance by 2040.
This article introduces ROSA, a Robot Operating System (ROS) framework designed to seamlessly integrate Large Language Models (LLMs) into embodied AI systems. ROSA addresses the challenges of connecting LLMs to robotic hardware by providing a standardized interface for perception, planning, and action.
The framework utilizes a prompt-based approach, converting robot tasks into natural language prompts for the LLM. This allows for flexible task specification and reasoning.
ROSA also includes tools for managing LLM outputs, ensuring safe and reliable robot behavior. The authors demonstrate ROSA’s capabilities through various experiments, showcasing its potential for creating more intelligent and adaptable robots.
The framework utilizes a prompt-based approach, converting robot tasks into natural language prompts for the LLM. This allows for flexible task specification and reasoning.
ROSA also includes tools for managing LLM outputs, ensuring safe and reliable robot behavior. The authors demonstrate ROSA’s capabilities through various experiments, showcasing its potential for creating more intelligent and adaptable robots.
This research introduces a novel robot operating system (ROS) framework designed to seamlessly integrate large language models (LLMs) into embodied artificial intelligence. The framework enables robots to interpret and execute natural language instructions with greater versatility and reliability.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
Sowbot is an open robotics platform designed to scale regenerative agriculture by providing accessible, lightweight robotics to researchers and farmers. It aims to bridge the "prototype gap" in agricultural robotics with a Reference Hardware Design and a Production-Ready Software Stack. The system comprises an 'Open Core' compute unit with dedicated boards for control/safety and perception/AI, powered by open-source software like Lizard, RoSys, and DevKit ROS. Various platforms like Sowbot, Sowbot Mini, and Sowbot Pico cater to different development stages, all emphasizing modularity, open-hardware, and a commitment to sustainability.
We designed and built a 12 degree-of-freedom (3 servos per leg × 4 legs) quadruped robot controlled by a Raspberry Pi Pico W, featuring integrated environmental sensing and a wireless WiFi controller. Starting from a custom CAD body and 3D-printed frame, the robot combines mechanical engineering and embedded electrical engineering to create a platform capable of coordinated four-legged locomotion, heading determination, environmental mapping, and target detection. In order to do so, our system leverages several sensors including an IMU, a solid state LiDAR sensor, and a contact-less infrared sensor.
Documentation for the Hiwonder SpiderPi Pro robotic arm, covering getting ready, quick user experience, remote desktop installation, PC software & programming, kinematics, AI visual recognition & tracking, AI visual transporting & kicking ball, group control, and network configuration.
SpiderPi Pro is an advanced hexapod robot integrated with AI vision and powered by Raspberry Pi. It features intelligent serial bus servos with a torque of 20KG, 5DOF robot arm, glowy ultrasonic sensor, IMU sensor and dot matrix module and can be programmed using Python. SpiderPi Pro serves as an ideal platform for conducting research in motion control for hexapod robots, machine vision, OpenCV, deep learning, and various other fields.
