# Autonomous Navigation for Quadrupedal Robots with Optimized Jumping through Constrained Obstacles

arXiv: https://arxiv.org/abs/2107.00773 · HTML: https://mlsanity.com/paper/2107.00773/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control](https://mlsanity.com/paper/1909.06586/) (2019)
- [Optimization-Based Collision Avoidance](https://mlsanity.com/paper/1711.03449/) (2017)
- [Robotic Guide Dog: Leading a Human with Leash-Guided Hybrid Physical Interaction](https://mlsanity.com/paper/2103.14300/) (2021)
- [Motion Planning for Quadrupedal Locomotion: Coupled Planning, Terrain Mapping and Whole-Body Control](https://mlsanity.com/paper/2003.05481/) (2020)
- [Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds](https://mlsanity.com/paper/1607.04537/) (2016)
- [Dynamic Legged Manipulation of a Ball Through Multi-Contact Optimization](https://mlsanity.com/paper/2008.00191/) (2020)
- [Contact Planning for the ANYmal Quadruped Robot using an Acyclic Reachability-Based Planner](https://mlsanity.com/paper/1904.08238/) (2019)

## Built on by (most cited first, 6 of 6)

- [Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot](https://mlsanity.com/paper/2208.01160/) (2022)
- [Robust and Versatile Bipedal Jumping Control through Reinforcement Learning](https://mlsanity.com/paper/2302.09450/) (2023)
- [Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning](https://mlsanity.com/paper/2210.04435/) (2022)
- [Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation](https://mlsanity.com/paper/2204.08647/) (2022)
- [Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots](https://mlsanity.com/paper/2206.14424/) (2022)
- [Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.10514/) (2026)

## Abstract (arXiv)

Quadrupeds are strong candidates for navigating challenging environments because of their agile and dynamic designs. This paper presents a methodology that extends the range of exploration for quadrupedal robots by creating an end-to-end navigation framework that exploits walking and jumping modes. To obtain a dynamic jumping maneuver while avoiding obstacles, dynamically-feasible trajectories are optimized offline through collocation-based optimization where safety constraints are imposed. Such optimization schematic allows the robot to jump through window-shaped obstacles by considering both obstacles in the air and on the ground. The resulted jumping mode is utilized in an autonomous navigation pipeline that leverages a search-based global planner and a local planner to enable the robot to reach the goal location by walking. A state machine together with a decision making strategy allows the system to switch behaviors between walking around obstacles or jumping through them. The proposed framework is experimentally deployed and validated on a quadrupedal robot, a Mini Cheetah, to enable the robot to autonomously navigate through an environment while avoiding obstacles and jumping over a maximum height of 13 cm to pass through a window-shaped opening in order to reach its goal.