仅需Python基础，从0构建自己的具身智能机器人；从0逐步构建VLA/OpenVLA/SmolVLA/Pi0， 深入理解具身智能

ROS-LLM is a framework designed for embodied intelligence applications in ROS. It allows natural language interactions and leverages Large Language Models (LLMs) for decision-making and robot control. With an easy configuration process, this framework allows for swift integration, enabling your robot to operate with it in as little as ten minutes.

[ACM CSUR 2025] Understanding World or Predicting Future? A Comprehensive Survey of World Models

UMI-3D SLAM and Data Processing Pipeline: https://umi-3d.github.io/

[AAAI 2026 Oral] FreeAskWorld is an interactive simulation framework that integrates large language models (LLMs) for high-level planning and socially grounded interaction in embodied AI.

[Tutorial] The Embodied Intelligence Introductory Practice of OpenMOSS Lab, SII&Fudan

OceanGym: A Benchmark Environment for Underwater Embodied Agents

An Open-World Foundation Model for General-Purpose Embodied Intelligence.

LabVLA: Grounding Vision–Language–Action Models in Scientific Laboratories

Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning

The repo contains the code and dataset for the World Models Track of GigaBrain Challenge 2026 CVPR Workshop.

Real-world deployment of Octo on UR5e/UR3e robots — the first open-source end-to-end pipeline covering teleoperation, RLDS data collection, 98-episode real-robot dataset, model finetuning, and distributed GPU↔robotinference. All pure Python.

Parameter-Efficient Fine-Tuning For Edge-Cloud Collabrative Computing

[Embodied-Intelligent-Industrial-Robotics-Survey-2025] Paper List for Embodied Intelligent Industrial Robotics (EIIR)

🦞 The World's First Sensor-Agnostic Tactile Data Annotation Toolkit — Load any tactile sensor, annotate visually, export a unified schema

Code for "Evolution and learning in differentiable robots", Strgar et al., Proceedings of Robotics: Science and Systems (RSS) 2024

Embodied-OS：面向具身智能的分层运行时——Driver·Capability·Skill·Brain 全栈参考实现

Code for "Accelerated co-design of robots through morphological pretraining", Strgar & Kriegman, Proceedings of the International Conference on Learning Representations (ICLR) 2026

Intelligent Manipulation and Perception for Robotic Arms

Website Convergenge + space members Auth0 / on Github Pages