# Autonomous Excavation of Challenging Terrain using Oscillatory Primitives and Adaptive Impedance Control

arXiv: https://arxiv.org/abs/2409.18273 · HTML: https://mlsanity.com/paper/2409.18273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2024.

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

- [Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation](https://mlsanity.com/paper/2609.12677/) (2026)

## Abstract (arXiv)

This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a standard penetrate-drag-scoop motion pattern. Inspired by human excavation strategies, our approach incorporates oscillatory rotation elements -- including swivel, twist, and dive motions -- to break up compacted, tangled grains and reduce jamming. We also present an adaptive impedance control method, the Reactive Attractor Impedance Controller (RAIC), that adapts a motion trajectory to unexpected forces during loading in a manner that tracks a trajectory closely when loads are low, but avoids excessive loads when significant resistance is met. Our method is evaluated on four terrains using a robotic arm, demonstrating improved excavation performance across multiple metrics, including volume scooped, protective stop rate, and trajectory completion percentage.