# Berkeley Humanoid: A Research Platform for Learning-based Control

arXiv: https://arxiv.org/abs/2407.21781 · HTML: https://mlsanity.com/paper/2407.21781/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2024.

## Built on by (most cited first, 24 of 24)

- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [ExBody2: Advanced Expressive Humanoid Whole-Body Control](https://mlsanity.com/paper/2412.13196/) (2024)
- [HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit](https://mlsanity.com/paper/2502.13013/) (2025)
- [OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation](https://mlsanity.com/paper/2410.11792/) (2024)
- [AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control](https://mlsanity.com/paper/2505.03738/) (2025)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies](https://mlsanity.com/paper/2410.11825/) (2024)
- [HuB: Learning Extreme Humanoid Balance](https://mlsanity.com/paper/2505.07294/) (2025)
- [CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models](https://mlsanity.com/paper/2409.18382/) (2024)
- [Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking](https://mlsanity.com/paper/2412.17730/) (2024)
- [Towards Embodiment Scaling Laws in Robot Locomotion](https://mlsanity.com/paper/2505.05753/) (2025)
- [Olaf: Bringing an Animated Character to Life in the Physical World](https://mlsanity.com/paper/2512.16705/) (2025)
- [Let Humanoids Hike! Integrative Skill Development on Complex Trails](https://mlsanity.com/paper/2505.06218/) (2025)
- [Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics](https://mlsanity.com/paper/2502.07380/) (2025)
- [The Duke Humanoid: Design and Control For Energy Efficient Bipedal Locomotion Using Passive Dynamics](https://mlsanity.com/paper/2409.19795/) (2024)
- [LiPS: Large-Scale Humanoid Robot Reinforcement Learning with Parallel-Series Structures](https://mlsanity.com/paper/2503.08349/) (2025)
- [Learning Bipedal Locomotion on Gear-Driven Humanoid Robot Using Foot-Mounted IMUs](https://mlsanity.com/paper/2504.00614/) (2025)
- [AURA: Autonomous Upskilling with Retrieval-Augmented Agents](https://mlsanity.com/paper/2506.02507/) (2025)
- [PolySim: Bridging the Sim-to-Real Gap for Humanoid Control via Multi-Simulator Dynamics Randomization](https://mlsanity.com/paper/2510.01708/) (2025)
- [Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice](https://mlsanity.com/paper/2411.15350/) (2024)
- [Robust RL Control for Bipedal Locomotion with Closed Kinematic Chains](https://mlsanity.com/paper/2507.10164/) (2025)
- [Fauna Sprout: A lightweight, approachable, developer-ready humanoid robot](https://mlsanity.com/paper/2601.18963/) (2026)
- [Continual Uncertainty Learning for Robust Control of Nonlinear Systems with Multiple Heterogeneous Uncertainties](https://mlsanity.com/paper/2602.17174/) (2026)
- [Comparative Study on Agility, Efficiency, and Impact Absorption of Bipedal Robots with Active Toes](https://mlsanity.com/paper/2606.19699/) (2026)

## Abstract (arXiv)

We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid research platform for learning-based control. Our lightweight, in-house-built robot is designed specifically for learning algorithms with low simulation complexity, anthropomorphic motion, and high reliability against falls. The robot's narrow sim-to-real gap enables agile and robust locomotion across various terrains in outdoor environments, achieved with a simple reinforcement learning controller using light domain randomization. Furthermore, we demonstrate the robot traversing for hundreds of meters, walking on a steep unpaved trail, and hopping with single and double legs as a testimony to its high performance in dynamical walking. Capable of omnidirectional locomotion and withstanding large perturbations with a compact setup, our system aims for scalable, sim-to-real deployment of learning-based humanoid systems. Please check http://berkeley-humanoid.com for more details.