In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving autonomous excavation using reinforcement learning (RL) through a rock excavation simulator. In the simulation, resolution can be defined by the particle size/number in the whole soil space. Fine-resolution simulations closely mimic real-world behavior but demand significant calculation time and challenging sample collection, while coarse-resolution simulations enable faster sample collection but deviate from real-world behavior. To combine the advantages of both resolutions, we explore using policies developed in coarse-resolution simulations for pre-training in fine-resolution simulations. To this end, we propose a novel policy learning framework called Progressive-Resolution Policy Distillation (PRPD), which progressively transfers policies through some middle-resolution simulations with conservative policy transfer to avoid domain gaps that could lead to policy transfer failure. Validation in a rock excavation simulator and nine real-world rock environments demonstrated that PRPD reduced sampling time to less than 1/7 while maintaining task success rates comparable to those achieved through policy learning in a fine-resolution simulation. Note to Practitioners—This paper is motivated by the issue of computation time in excavation simulation using soil particles. The behavior of real soil is highly complex, and approximating it at high resolution requires enormous computational costs. Therefore, existing soil simulators have focused on improving simulation accuracy while maintaining reduced computation time. This paper takes a different approach by focusing on the learning of control policies in excavation simulators and proposes a framework for reducing calculation time in such use cases. In this framework, a control policy is first learned in a low-resolution simulation, significantly reducing computation time. The learned policy is then transferred to a high-resolution simulation for retraining, thereby achieving an overall reduction in simulation time. Furthermore, to enable robust policy transfer across different resolutions, this paper discusses a stable policy distillation scheme and insights into resolution design. This approach enables the development of autonomous excavation systems without relying on expensive real-world data collection, improving the scalability and adaptability of autonomous excavation. Simulation experiments suggest that this framework significantly reduces training time compared to conventional policy learning approaches. However, real-world validation has so far been limited to simple excavation robots. Future research will explore applications to excavators and other machinery more suitable for real-world operations. Although this paper focuses on autonomous excavation, the proposed approach can also be extended to environments where increased simulation resolution critically impacts computation time, such as liquid and soft object manipulation.

Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that policy stabilization can be learned via simple augmentations. In real-world scenarios, demonstrations are often of diverse-quality, and disturbance injection instead learns sub-optimal policies that fail to replicate desired behavior. To address this issue, this paper proposes a novel imitation learning framework that combines both policy robustification and optimal demonstration learning. Specifically, this combinatorial approach forces policy learning and disturbance injection optimization to focus on mainly learning from high task achievement demonstrations, while utilizing low achievement ones to decrease the number of samples needed. The effectiveness of the proposed method is verified through experiments using an excavation task in both simulations and a real robot, resulting in high-achieving policies that are more stable and robust to diverse-quality demonstrations. In addition, this method utilizes all of the weighted sub-optimal demonstrations without eliminating them, resulting in practical data efficiency benefits.

Avoiding obstacles is one of the essential functions of outdoor mobile robots. Detection of three-dimensional obstacles and avoidance methods have been studied actively. However, planar obstacles, such as puddles, have been ignored generally. Avoiding puddles is important to prevent electrical problems, odometry error, and feeling displeasure. This paper proposes a method to detect puddles by using laser reflection intensity obtained from 2D LRF and a method for path planning to avoid the puddles. Laser reflection intensities of several road surfaces are investigated, and the effectiveness of the proposed methods are confirmed through experiments with a real-robot.