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PyRep is a toolkit for robot learning research, built on top of the virtual robotics experimentation platform (V-REP). Through a series of modifications and additions, we have created a tailored version of V-REP built with robot learning in mind. The new PyRep toolkit offers three improvements: (1) a simple and flexible API for robot control and scene manipulation, (2) a new rendering engine, and (3) speed boosts upwards of 10,000x in comparison to the previous Python Remote API. With these improvements, we believe PyRep is the ideal toolkit to facilitate rapid prototyping of learning algorithms in the areas of reinforcement learning, imitation learning, state estimation, mapping, and computer vision.

The procedure for humanitarian land mine removal varies greatly. However, one central element is detecting and marking suspected mine locations using sensors such as trained dogs and metal detectors. This step is expensive, tedious, and dangerous. In order to improve the land mine detection process, a reliable, low-cost robotic manipulation system has been constructed. The system consists of long-reach manipulator mounted on a commercially available All-Terrain Vehicle. The system is capable of autonomously scanning for mines with different types of sensors. An electromagnetic induction metal detector is used in conjunction with ground-penetrating radar to detect metal and larger heterogeneities in the ground, i.e. mine detonators and mine casings, respectively. This sensor combination greatly reduces the number of false alarms over the traditional single-sensor approach. Given the long reach of the arm, the endpoint is susceptible to unwanted oscillations that corrupt the scanning information. This paper describes a control system that utilizes input shaping to improve endpoint tracking. The result is a system that generates high-quality sensor information for precise mine identification.