# Learning Tool-Aware Adaptive Compliant Control for Autonomous Regolith Excavation

arXiv: https://arxiv.org/abs/2509.05475 · HTML: https://mlsanity.com/paper/2509.05475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation](https://mlsanity.com/paper/2110.00704/) (2021)
- [Excavation Reinforcement Learning Using Geometric Representation](https://mlsanity.com/paper/2201.11292/) (2022)
- [GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2303.05193/) (2023)
- [MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments](https://mlsanity.com/paper/2107.09996/) (2021)
- [Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media](https://mlsanity.com/paper/2508.11503/) (2025)
- [Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space](https://mlsanity.com/paper/2405.01134/) (2024)

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

- [Reward-Free Continual Adaptation for Resilient Space Robots](https://mlsanity.com/paper/2608.23452/) (2026)

## Abstract (arXiv)

Autonomous regolith excavation is a cornerstone of in-situ resource utilization for a sustained human presence beyond Earth. However, this task is fundamentally hindered by the complex interaction dynamics of granular media and the operational need for robots to use diverse tools. To address these challenges, this work introduces a framework where a model-based reinforcement learning agent learns within a parallelized simulation. This environment leverages high-fidelity particle physics and procedural generation to create a vast distribution of both lunar terrains and excavation tool geometries. To master this diversity, the agent learns an adaptive interaction strategy by dynamically modulating its own stiffness and damping at each control step through operational space control. Our experiments demonstrate that training with a procedural distribution of tools is critical for generalization and enables the development of sophisticated tool-aware behavior. Furthermore, we show that augmenting the agent with visual feedback significantly improves task success. These results represent a validated methodology for developing the robust and versatile autonomous systems required for the foundational tasks of future space missions.