Rock capturing with standard excavator buckets is a challenging task typically requiring the expertise of skilled 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 methods focus mainly on continuous media or rely on specialized grippers, limiting their applicability to real-world construction sites. This paper introduces a fully data-driven control framework for rock capturing that eliminates the need for explicit modeling of rock or soil properties. A model-free reinforcement learning agent is trained in the AGX Dynamics simulator using the Proximal Policy Optimization (PPO) algorithm and a guiding reward formulation. The learned policy outputs joint velocity commands directly to the boom, arm, and bucket of a CAT365 excavator model. Robustness is enhanced through extensive domain randomization of rock geometry, density, and mass, as well as the initial configurations of the bucket, rock, and goal position. To the best of our knowledge, this is the first study to develop and evaluate an RL-based controller for the rock capturing task. Experimental results show that the policy generalizes well to unseen rocks and varying soil conditions, achieving high success rates comparable to those of human participants while maintaining machine stability. These findings demonstrate the feasibility of learning-based excavation strategies for discrete object manipulation without requiring specialized hardware or detailed material models.

翻译：使用标准挖掘机铲斗进行岩石抓取是一项具有挑战性的任务，通常需要熟练操作员的专业知识。与土壤挖掘不同，它涉及在非结构化环境中操纵大型不规则岩石，其中与颗粒材料的复杂接触交互使得基于模型的控制方法不切实际。现有的自主挖掘方法主要关注连续介质或依赖专用抓取器，限制了其在真实建筑工地上的适用性。本文提出了一种完全数据驱动的岩石抓取控制框架，无需对岩石或土壤属性进行显式建模。在AGX Dynamics模拟器中，使用近端策略优化（PPO）算法和引导奖励公式训练了一个无模型强化学习智能体。学习到的策略直接向CAT365挖掘机模型的大臂、小臂和铲斗输出关节速度指令。通过对岩石几何形状、密度和质量，以及铲斗、岩石和目标位置的初始配置进行广泛的领域随机化，增强了系统的鲁棒性。据我们所知，这是首个针对岩石抓取任务开发并评估基于强化学习的控制器的研究。实验结果表明，该策略能够很好地泛化到未见过的岩石和变化的土壤条件，在保持机器稳定性的同时，实现了与人类参与者相当的高成功率。这些发现证明了无需专用硬件或详细材料模型，即可通过基于学习的方法实现离散物体操纵的挖掘策略的可行性。