# Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2106.09357 · HTML: https://mlsanity.com/paper/2106.09357/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2021.

## Built on by (most cited first, 17 of 17)

- [Advanced Skills by Learning Locomotion and Local Navigation End-to-End](https://mlsanity.com/paper/2209.12827/) (2022)
- [Learning and Adapting Agile Locomotion Skills by Transferring Experience](https://mlsanity.com/paper/2304.09834/) (2023)
- [Symmetry Considerations for Learning Task Symmetric Robot Policies](https://mlsanity.com/paper/2403.04359/) (2024)
- [Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion](https://mlsanity.com/paper/2310.17634/) (2023)
- [Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design](https://mlsanity.com/paper/2401.16337/) (2024)
- [Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning](https://mlsanity.com/paper/2412.05515/) (2024)
- [Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning](https://mlsanity.com/paper/2202.03957/) (2022)
- [PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots](https://mlsanity.com/paper/2407.04224/) (2024)
- [Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey](https://mlsanity.com/paper/2404.17070/) (2024)
- [Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions](https://mlsanity.com/paper/2410.00490/) (2024)
- [AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control](https://mlsanity.com/paper/2503.21401/) (2025)
- [Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control](https://mlsanity.com/paper/2503.03574/) (2025)
- [Learning Rapid Turning, Aerial Reorientation, and Balancing using Manipulator as a Tail](https://mlsanity.com/paper/2407.10420/) (2024)
- [Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing](https://mlsanity.com/paper/2509.12776/) (2025)
- [Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization](https://mlsanity.com/paper/2301.04049/) (2023)
- [GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots](https://mlsanity.com/paper/2509.17582/) (2025)
- [Towards Low-Gravity Planetary Exploration using Reinforcement Learning for Walking, Jumping, and In-flight Attitude Control](https://mlsanity.com/paper/2605.24643/) (2026)

## Abstract (arXiv)

In this article, we show that learned policies can be applied to solve legged locomotion control tasks with extensive flight phases, such as those encountered in space exploration. Using an off-the-shelf deep reinforcement learning algorithm, we trained a neural network to control a jumping quadruped robot while solely using its limbs for attitude control. We present tasks of increasing complexity leading to a combination of three-dimensional (re-)orientation and landing locomotion behaviors of a quadruped robot traversing simulated low-gravity celestial bodies. We show that our approach easily generalizes across these tasks and successfully trains policies for each case. Using sim-to-real transfer, we deploy trained policies in the real world on the SpaceBok robot placed on an experimental testbed designed for two-dimensional micro-gravity experiments. The experimental results demonstrate that repetitive, controlled jumping and landing with natural agility is possible.