Data-driven rock capture with a standard excavator via model-free reinforcement learning

Rock capturing with standard excavator buckets is a challenging task that typically requires skilled human operators. Unlike soil digging, it involves manipulating large, irregular rocks in unstructured environments, where complex contact interactions with granular material make model-based control impractical. Existing autonomous excavation approaches mainly target continuous media or rely on specialized grippers, limiting their applicability in real construction settings. This paper presents a fully data-driven control framework for rock capturing that avoids explicit modeling of rock or soil properties. A model-free reinforcement learning agent is trained in the AGX Dynamics® simulator using Proximal Policy Optimization (PPO) and a guiding reward formulation. The learned policy outputs joint velocity commands directly to the boom, arm, and bucket of a CAT®365 excavator model. Robustness is improved through extensive domain randomization of rock properties and initial configurations. Results show generalization to unseen rocks and soil conditions, achieving high success rates while maintaining machine stability.