# RP1: An Open Source Full-Stack Platform for Agentic Humanoids > Source: > Published: 2026-10-16 08:05:39+00:00 **English**   /   [简体中文](https://github.com/Roboparty/RP1/blob/main/README.zh-CN.md) From mechanical design and electronics to firmware, control and learning. Built to be understood. Open to be built upon. **RP1 is RoboParty's fully open-source humanoid platform: a shared foundation for building, understanding and extending a real robot.** The platform brings mechanical and electrical design, joint modules, firmware, robot models, deployment and learning into one connected development stack. Explore a single subsystem, reproduce a build, or develop your own control and learning workflows. | Hardware you can inspect | Software you can extend | Intelligence you can explore | |---|---|---| | Mechanical CAD, schematics, PCB layouts, Gerber files, BOM and assembly documentation. | Linux BSP, communication, device interfaces, robot descriptions and deployment modules. | Locomotion training, motion imitation, tracking and behavior-model projects. | RP1, photographed in the real world. **No black boxes, nothing withheld — full control handed back to the developer** Release contents, supported configurations and setup instructions are maintained in each repository. Contributions belong in the relevant sub-repository. This overview repository does not accept pull requests; please open issues and pull requests in the project you are working on. | Repository | Description | |---|---| | [**rp1_hardware**](https://github.com/Roboparty/rp1_hardware) | Mechanical source files, electronics, testing and assembly SOPs. | | [**ROMOMO**](https://github.com/Roboparty/ROMOMO) | Joint modules, firmware and host tools. | | [**RP-hand**](https://github.com/Roboparty/rp_dexhand) | Dexterous hand wiring, CAN-FD communication and SDK integration. | | [**RP1_TEST**](https://github.com/Roboparty/rp1_test) | RP1 full-machine testing. | | [**robot_supplychain**](https://github.com/Roboparty/robot_supplychain) | Component selection, inspection, quality gates and acceptance. | | [**roboparty_joystick**](https://github.com/Roboparty/roboparty_joystick) | RoboParty controller and app. | ## **Robot configurations** See the description repository's current release for supported robot configurations. Supported configurations include 24, 28, 30 and 42 DoF variants. | Repository | Description | |---|---| | [**roboparty_deploy**](https://github.com/Roboparty/roboparty_deploy) | Runtime SDK for IMU, motor, dexhand, inference and camera modules. | | [**RP1_description**](https://github.com/Roboparty/RP1_description) | URDF, MJCF, 3D assets and robot configurations. | | [**roboparty_train**](https://github.com/Roboparty/roboparty_train) | Locomotion, motion imitation and behavior foundation models. | | [**RP1_firmware**](https://github.com/Roboparty/RP1_firmware) | Linux BSP, USB-CAN bridge, low-level SDK and packages. | Coming soon. | Project | Description | |---|---| | [**AMP**](https://github.com/Roboparty/roboparty_train) | Learns motion priors from reference motions through adversarial training, supporting natural-looking locomotion and control. | | [**BeyondMimic**](https://github.com/Roboparty/roboparty_train) | Tracks reference motions to reproduce expressive whole-body movements, including dance and motion-imitation sequences. | | [**MimicLite**](https://github.com/Roboparty/MimicLite) | Focuses on reference-motion tracking, translating target movements into coordinated whole-body robot actions. | | [**UFO**](https://github.com/Roboparty/UFO) | Explores a shared behavior foundation for learning and controlling a range of humanoid movements. | | [**PBFM**](https://github.com/Roboparty/P-BFM) | Connects environmental perception with a behavior foundation model to study movement in relation to the surrounding scene. | | [**IBFM**](https://github.com/Roboparty/I-BFM-Code) | Focuses on whole-body interaction with the environment, exploring coordinated movements for contact-rich tasks. | | [**INTACT-JEPA**](https://github.com/Roboparty/INTACT-JEPA) | Learns an intent-to-action mapping with JEPA, translating visual goals into control actions without online search. | ## **Related papers** - **[P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning](https://arxiv.org/abs/2610.15833)** — Xue Wang, Yitang Li, Yiming Zhang, Siqin Wang, Junhan Sun, Zhihao Liu, Yaojie Shen, Jingbo Yuan, Hao Zhao, Qingdu Li.*ICLR 2027* - **[I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning](https://arxiv.org/abs/2610.06129)** — Ziqi Han, Yitang Li, Junhan Sun, Fanrong Dong, Yaojie Shen, Lei Ye, Zetong Jing, Yongqi Zhang, Yiming Zhang, Xue Wang, Hao Zhao.*ICRA* - **[PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control](https://arxiv.org/abs/2609.24840)** — Lei Ye, Haibo Gao, Yitang Li, Peng Xu, Zetong Jing, Junhan Sun, Fanrong Dong, Ziqi Han, Xue Wang, Jianhua Sun, Cewu Lu, Hao Zhao, Liang Ding.*ICRA* - **[From Hitter to Player: Separating Motor Execution and Strategy in Competitive Humanoid Table Tennis](https://arxiv.org/abs/2610.14067)** — Fanrong Dong, Ziyi Sun, Ziqi Han, Junhan Sun, Lei Ye, Zetong Jing, Hao Zhao, Shaoxun Liu, Rongrong Wang.*ICRA* - **[INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models](https://arxiv.org/abs/2607.26056)** — Junhan Sun, Hao Zhao, Guofeng Zhang.*ICLR* - **[VIStream: Bringing Inertial Sensing to Streaming Geometric Foundation Models](https://github.com/CN-MingZheLiu/vistream)** — Mingzhe Liu.*ICRA* Meet the community, share your work, and connect with RoboParty. **RoboParty** Open hardware. Open intelligence. Shared possibility. [GPL-3.0](https://github.com/Roboparty/RP1/blob/main/LICENSE)   ·   © [RoboParty](https://roboparty.com)   ·   [Back to top](#top) See each sub-repository for its applicable license.