mc-mujoco: Simulating Articulated Robots with FSM Controllers in MuJoCo
Not on the map. Why?
Abstract (arXiv)
For safe and reliable deployment of any robot controller on the real hardware platform, it is generally a necessary practice to comprehensively assess the performance of the controller with the specific robot in a realistic simulation environment beforehand. While there exist several software solutions that can provide the core physics engine for this purpose, it is often a cumbersome and error-prone effort to interface the simulation environment with the robot controller being evaluated. The controller may have a complex structure consisting of multiple states and transitions within a finite-state machine (FSM), and may even require input through a GUI. In this work, we present mc-mujoco -- an open-source software framework that forms an interface between the MuJoCo physics simulator and the mc-rtc robot control framework. We provide implementation details and describe the process for adding support for essentially any new robot. We also demonstrate and publish a sample FSM controller for bipedal locomotion and stable grasping of a rigid object by the HRP-5P humanoid robot in MuJoCo. The code and usage instructions for mc-mujoco, the developed robot modules, and the FSM controller are available online.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Sep 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 2.
5 papers.
- 2023Learning Bipedal Walking for Humanoids with Current Feedbackcited by 18
- 2025Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learningcited by 3
- 2026Safe Execution of RL Policies Via Acceleration-Based CBF-QP Constraint Enforcement for Real-World Robotic Deploymentscited by 1
- 2025HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mambacited by 0
- 2026Towards An LLM-Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systemscited by 0
- 2026Towards An LLM-Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systems24 Sep 2026
- 2026Safe Execution of RL Policies Via Acceleration-Based CBF-QP Constraint Enforcement for Real-World Robotic Deployments16 Jul 2026
- 2025HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba22 Sep 2025
- 2025Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learning18 Apr 2025
- 2023Learning Bipedal Walking for Humanoids with Current Feedback7 Mar 2023