# Learning Rock Pushability on Rough Planetary Terrain

arXiv: https://arxiv.org/abs/2505.09833 · HTML: https://mlsanity.com/paper/2505.09833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 13 of 13)

- [Exploring Event Camera-based Odometry for Planetary Robots](https://mlsanity.com/paper/2204.05880/) (2022)
- [A Survey of Robot Manipulation in Contact](https://mlsanity.com/paper/2112.01942/) (2021)
- [GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion](https://mlsanity.com/paper/2309.15459/) (2023)
- [Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing](https://mlsanity.com/paper/2307.14272/) (2023)
- [Goal-Driven Robotic Pushing Using Tactile and Proprioceptive Feedback](https://mlsanity.com/paper/2012.01859/) (2020)
- [Object and Relation Centric Representations for Push Effect Prediction](https://mlsanity.com/paper/2102.02100/) (2021)
- [Pushing in the Dark: A Reactive Pushing Strategy for Mobile Robots Using Tactile Feedback](https://mlsanity.com/paper/2403.09305/) (2024)
- [Improved Planetary Rover Inertial Navigation and Wheel Odometry Performance through Periodic Use of Zero-Type Constraints](https://mlsanity.com/paper/1906.08849/) (2019)
- [Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions](https://mlsanity.com/paper/2109.05509/) (2021)
- [Force Push: Robust Single-Point Pushing with Force Feedback](https://mlsanity.com/paper/2401.17517/) (2024)
- [Slip-Based Autonomous ZUPT through Gaussian Process to Improve Planetary Rover Localization](https://mlsanity.com/paper/2103.07587/) (2021)
- [Multi-Object Graph Affordance Network: Goal-Oriented Planning through Learned Compound Object Affordances](https://mlsanity.com/paper/2309.10426/) (2023)
- [Proprioceptive Slip Detection for Planetary Rovers in Perceptually Degraded Extraterrestrial Environments](https://mlsanity.com/paper/2207.13629/) (2022)

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

- [Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation](https://mlsanity.com/paper/2606.07506/) (2026)

## Abstract (arXiv)

In the context of mobile navigation in unstructured environments, the predominant approach entails the avoidance of obstacles. The prevailing path planning algorithms are contingent upon deviating from the intended path for an indefinite duration and returning to the closest point on the route after the obstacle is left behind spatially. However, avoiding an obstacle on a path that will be used repeatedly by multiple agents can hinder long-term efficiency and lead to a lasting reliance on an active path planning system. In this study, we propose an alternative approach to mobile navigation in unstructured environments by leveraging the manipulation capabilities of a robotic manipulator mounted on top of a mobile robot. Our proposed framework integrates exteroceptive and proprioceptive feedback to assess the push affordance of obstacles, facilitating their repositioning rather than avoidance. While our preliminary visual estimation takes into account the characteristics of both the obstacle and the surface it relies on, the push affordance estimation module exploits the force feedback obtained by interacting with the obstacle via a robotic manipulator as the guidance signal. The objective of our navigation approach is to enhance the efficiency of routes utilized by multiple agents over extended periods by reducing the overall time spent by a fleet in environments where autonomous infrastructure development is imperative, such as lunar or Martian surfaces.