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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 | Mechanical source files, electronics, testing and assembly SOPs. |
| ROMOMO | Joint modules, firmware and host tools. |
| RP Hand | Dexterous hand product documentation, wiring, companion applications, CAD and robot models. Includes the C++ / Python / ROS 2 SDK entry point. |
| RP1_TEST | RP1 full-machine testing. |
| 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 | Deployment framework for RPO and RP1, with easy-to-use SDKs for imu, motor, inference and camera modules, callable from C++ and Python. |
| RP1_description | URDF and MJCF models, 3D meshes, and robot and RP Hand configurations. |
| roboparty_train | Isaac Lab training workspace for RPO locomotion policies, covering AMP, BeyondMimic and Parkour workflows with RSL-RL training and motion retargeting. |
| RP1_firmware | Linux BSP, USB-CAN bridge, low-level SDK and packages. |
| Project | Description |
|---|---|
| AMP | Learns motion priors from reference motions via adversarial training, enabling natural-looking locomotion and control. |
| BeyondMimic | Reproduces expressive whole-body movements by tracking reference motions, including dance and motion-imitation sequences. |
| Parkour | Learns perceptive locomotion policies to traverse challenging terrain, including stairs, gaps and obstacles. |
| MimicLite | Provides an efficient, general-purpose humanoid motion-tracking system for training deployable PPO and PPO-ROA policies. |
| UFO | Provides a general unsupervised reinforcement learning framework for humanoid control, supporting FB/TeCH training, motion tracking and reward and goal inference. |
| PBFM | Maps motion references, target body states and rewards into a shared behavioral intent space, combining intent with depth perception for terrain-adaptive control. |
| IBFM (RP1 support coming soon) | Learns reward-conditioned robust humanoid interaction via unsupervised reinforcement learning, demonstrated on carry, push and kick box tasks. |
| INTACT-JEPA (RP1 support coming soon) | Learns an intent-to-action mapping with JEPA for goal-conditioned robot control without broad action search. |
Related papers
- P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning — 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 — 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 — 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 — 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 — Junhan Sun, Hao Zhao, Guofeng Zhang. ICLR
- VIStream: Bringing Inertial Sensing to Streaming Geometric Foundation Models — Mingzhe Liu. ICRA
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See each sub-repository for its applicable license.