# Robust Quadruped Jumping via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.07089 · HTML: https://mlsanity.com/paper/2011.07089/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 27 of 27)

- [Learning to Jump from Pixels](https://mlsanity.com/paper/2110.15344/) (2021)
- [CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion](https://mlsanity.com/paper/2211.00458/) (2022)
- [Learning and Adapting Agile Locomotion Skills by Transferring Experience](https://mlsanity.com/paper/2304.09834/) (2023)
- [Learning Torque Control for Quadrupedal Locomotion](https://mlsanity.com/paper/2203.05194/) (2022)
- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning](https://mlsanity.com/paper/2103.06484/) (2021)
- [Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design](https://mlsanity.com/paper/2401.16337/) (2024)
- [Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Locomotion](https://mlsanity.com/paper/2212.14400/) (2022)
- [SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience](https://mlsanity.com/paper/2409.13678/) (2024)
- [Continuous Versatile Jumping Using Learned Action Residuals](https://mlsanity.com/paper/2304.08663/) (2023)
- [Puppeteer and Marionette: Learning Anticipatory Quadrupedal Locomotion Based on Interactions of a Central Pattern Generator and Supraspinal Drive](https://mlsanity.com/paper/2302.13378/) (2023)
- [Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning](https://mlsanity.com/paper/2203.05973/) (2022)
- [Guided Reinforcement Learning for Omnidirectional 3D Jumping in Quadruped Robots](https://mlsanity.com/paper/2507.16481/) (2025)
- [End-to-End Reinforcement Learning for Torque Based Variable Height Hopping](https://mlsanity.com/paper/2307.16676/) (2023)
- [AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control](https://mlsanity.com/paper/2503.21401/) (2025)
- [A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control](https://mlsanity.com/paper/2203.03391/) (2022)
- [Reinforcement Learning from Wild Animal Videos](https://mlsanity.com/paper/2412.04273/) (2024)
- [Efficient Reinforcement Learning for Jumping Monopods](https://mlsanity.com/paper/2309.07038/) (2023)
- [Behavior evolution-inspired approach to walking gait reinforcement training for quadruped robots](https://mlsanity.com/paper/2409.16862/) (2024)
- [SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning](https://mlsanity.com/paper/2502.12674/) (2025)
- [GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control](https://mlsanity.com/paper/2601.20668/) (2026)
- [ContractionPPO: Certified Reinforcement Learning via Differentiable Contraction Layers](https://mlsanity.com/paper/2603.19632/) (2026)
- [EFGCL: Learning Dynamic Motion through Spotting-Inspired External Force Guided Curriculum Learning](https://mlsanity.com/paper/2605.10063/) (2026)
- [Explosive Jumping with Rigid and Articulated Soft Quadrupeds via Example Guided Reinforcement Learning](https://mlsanity.com/paper/2503.16197/) (2025)
- [Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility](https://mlsanity.com/paper/2504.14103/) (2025)
- [SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping](https://mlsanity.com/paper/2606.18625/) (2026)
- [WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots](https://mlsanity.com/paper/2607.24036/) (2026)

## Abstract (arXiv)

In this paper, we consider a general task of jumping varying distances and heights for a quadrupedal robot in noisy environments, such as off of uneven terrain and with variable robot dynamics parameters. To accurately jump in such conditions, we propose a framework using deep reinforcement learning that leverages and augments the complex solution of nonlinear trajectory optimization for quadrupedal jumping. While the standalone optimization limits jumping to take-off from flat ground and requires accurate assumptions of robot dynamics, our proposed approach improves the robustness to allow jumping off of significantly uneven terrain with variable robot dynamical parameters and environmental conditions. Compared with walking and running, the realization of aggressive jumping on hardware necessitates accounting for the motors' torque-speed relationship as well as the robot's total power limits. By incorporating these constraints into our learning framework, we successfully deploy our policy sim-to-real without further tuning, fully exploiting the available onboard power supply and motors. We demonstrate robustness to environment noise of foot disturbances of up to 6 cm in height, or 33% of the robot's nominal standing height, while jumping 2x the body length in distance.