Vector-Wangel/XLeRobot

Features

• Vision-Language-Action Models - Implements vision-language-action models that process multimodal inputs to predict robotic joint actions.
• Dual-Arm Mobile Robots - Provides a physical platform featuring a wheeled base and two robotic arms for AI manipulation tasks.
• Agentic LLM Frameworks - Integrates large language models to translate high-level natural language instructions into physical robot actions.
• Autonomous Task Execution - Executes reinforcement learning policies to perform complex tasks independently on robotic hardware.
• Teleoperation-Based Imitation Learning - Records synchronized camera streams and joint positions via teleoperation to create imitation learning datasets.
• Vision-Language-Action Training - Trains vision-language-action models and transformers on recorded datasets to automate complex manipulation tasks.
• Embodied AI Platforms - Provides an integrated platform for embodied intelligence research, combining AI models with physical hardware.
• Robotics Simulators - Provides a physics-based virtual space for verifying AI policies and robot movements before physical deployment.
• Policy Training Data Collection - Provides tools for recording physical movements and camera data to train neural network policies.
• Physics Simulation Environments - Provides a physics-based simulation environment to verify AI motion policies before hardware deployment.
• Teleoperation Systems - Ships a system for recording training data and controlling hardware remotely via VR or game controllers.
• Language and Robotics Integration - Translates natural language instructions into physical robotic actions using large language models.
• Object Detection and Tracking - Employs algorithms for identifying and maintaining the trajectory of objects to guide physical interactions.
• Edge AI Model Deployment - Deploys trained AI policies directly onto physical robot hardware for autonomous manipulation.
• Robot Policy Validations in Simulation - Tests trained AI policies within physically accurate virtual environments before physical deployment.
• VR Robotic Interfaces - Interfaces virtual reality systems with hardware for intuitive remote manual operation in simulated and physical environments.
• Modular Physical Architectures - Implements a modular physical architecture with a wheeled base and interchangeable arms and grippers.
• Mobile Base Integration - Sets up a stable two-wheeled platform with motors to enable physical movement in a workspace.
• Real-Time Actuator Mapping - Maps inputs from VR systems or game controllers to robot actuators for low-latency manual manipulation.
• Low-Cost Robot Fabrication - Utilizes 3D printing and affordable components to construct low-cost mobile dual-arm robots.
• Open-Source Robot Arm Hardware - Provides 3D-printable designs and assembly guides for creating affordable robotic arm manipulators.
• Mobile Robot Chassis - Assembles a hardware platform with a wheeled base and dual arms for testing embodied AI.
• Robot Hardware Customization - Provides guidelines and construction methods for the robot chassis, including wheel and shell integration.
• Robot Joint Control - Allows manual operation of joints and end-effectors using keyboards, game controllers, or VR hardware.
• Remote Hardware Controls - Operates robotic hardware in real-time using connected peripherals to minimize network latency.
• Visual Tracking Controllers - Uses real-time camera feedback and computer vision models to adjust motor movements based on object position.

Historique des stars