# The Duke Humanoid: Design and Control For Energy Efficient Bipedal Locomotion Using Passive Dynamics

arXiv: https://arxiv.org/abs/2409.19795 · HTML: https://mlsanity.com/paper/2409.19795/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 16 of 16)

- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning](https://mlsanity.com/paper/2408.14472/) (2024)
- [Fast and Efficient Locomotion via Learned Gait Transitions](https://mlsanity.com/paper/2104.04644/) (2021)
- [Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation](https://mlsanity.com/paper/2309.01952/) (2023)
- [Berkeley Humanoid: A Research Platform for Learning-based Control](https://mlsanity.com/paper/2407.21781/) (2024)
- [MPC for Humanoid Gait Generation: Stability and Feasibility](https://mlsanity.com/paper/1901.08505/) (2019)
- [Dynamic Loco-manipulation on HECTOR: Humanoid for Enhanced ConTrol and Open-source Research](https://mlsanity.com/paper/2312.11868/) (2023)
- [NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot](https://mlsanity.com/paper/1810.08395/) (2018)

## Built on by (most cited first, 2 of 2)

- [Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions](https://mlsanity.com/paper/2502.01465/) (2025)
- [LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning](https://mlsanity.com/paper/2504.15472/) (2025)

## Abstract (arXiv)

We present the Duke Humanoid, an open-source 10-degrees-of-freedom humanoid, as an extensible platform for locomotion research. The design mimics human physiology, with symmetrical body alignment in the frontal plane to maintain static balance with straight knees. We develop a reinforcement learning policy that can be deployed zero-shot on the hardware for velocity-tracking walking tasks. Additionally, to enhance energy efficiency in locomotion, we propose an end-to-end reinforcement learning algorithm that encourages the robot to leverage passive dynamics. Our experimental results show that our passive policy reduces the cost of transport by up to 50% in simulation and 31% in real-world tests. Our website is http://generalroboticslab.com/DukeHumanoidv1/ .