Reinforcement Learning Based Tactile Sensing for Active Point Cloud Acquisition, Recognition and Localization

Traditional passive point cloud acquisition systems, such as lidars or stereo cameras, can be impractical in real-life and industrial use cases. Firstly, some extreme environments may preclude the use of these sensors. Secondly, they capture information from the entire scene instead of focusing on areas relevant to the end task, such as object recognition and localization. In contrast, we propose to train a Reinforcement Learning (RL) agent with dual objectives: i) control a robot equipped with a tactile (or laser) sensor to iteratively collect a few relevant points from the scene, and ii) recognize and localize objects from the sparse point cloud which has been collected. The iterative point sampling strategy, referred to as an active sampling strategy, is jointly trained with the classifier and the pose estimator to ensure efficient exploration that focuses on areas relevant to the recognition task. To achive these two objectives, we introduce three RL reward terms: classification, exploration, and pose estimation rewards. These rewards serve the purpose of offering guidance and supervision in their respective domain, allowing us to delve into their individual impacts and contributions. We compare the proposed framework to both active sampling strategies and passive hard-coded sampling strategies coupled with state-of-the-art point cloud classifiers. Furthermore, we evaluate our framework in realistic scenarios, considering realistic and similar objects, as well as accounting for uncertainty in the object's position in the workspace.