# Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.07846 · HTML: https://mlsanity.com/paper/2409.07846/

## Key facts

- Cited by 1 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 8 of its 12 references that are in the map.
- First posted on arXiv 12 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, 8 of 8)

- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition](https://mlsanity.com/paper/2011.01387/) (2020)
- [Barkour: Benchmarking Animal-level Agility with Quadruped Robots](https://mlsanity.com/paper/2305.14654/) (2023)
- [DeepWalk: Omnidirectional Bipedal Gait by Deep Reinforcement Learning](https://mlsanity.com/paper/2106.00534/) (2021)
- [Learning Bipedal Walking for Humanoids with Current Feedback](https://mlsanity.com/paper/2303.03724/) (2023)
- [Learning Bipedal Walking On Planned Footsteps For Humanoid Robots](https://mlsanity.com/paper/2207.12644/) (2022)
- [Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX](https://mlsanity.com/paper/2407.05148/) (2024)

## Built on by (most cited first, 1 of 1)

- [Quadrupedal Robot Skateboard Mounting via Reverse Curriculum Learning](https://mlsanity.com/paper/2505.06561/) (2025)

## Abstract (arXiv)

Learning-based methods have proven useful at generating complex motions for robots, including humanoids. Reinforcement learning (RL) has been used to learn locomotion policies, some of which leverage a periodic reward formulation. This work extends the periodic reward formulation of locomotion to skateboarding for the REEM-C robot. Brax/MJX is used to implement the RL problem to achieve fast training. Initial results in simulation are presented with hardware experiments in progress.