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.
A paraphrasing of Gerald Jay Sussman's explanation for MIT's switch from Scheme to Python in its undergraduate computer science program, focusing on the changing nature of programming and the need to adapt to modern systems and libraries.
This article discusses the choice between Jetson Nano and Raspberry Pi 5 for building a first ROS2 robot, advocating for a Raspberry Pi 5-based kit like the MentorPi M1 to bypass hardware headaches and accelerate learning.
LingBot-VLA, a Vision-Language-Action model trained on extensive real-world robotic data, demonstrates superior performance and generalization across multiple platforms with enhanced efficiency. The model is supported by an efficient codebase and open access to code, base model, and benchmark data.
