It is difficult for robots to retrieve objects in densely cluttered lateral access scenes with movable objects as jamming against adjacent objects and walls can inhibit progress. We propose the use of two action primitives -- burrowing and excavating -- that can fluidize the scene to un-jam obstacles and enable continued progress. Even when these primitives are implemented in an open loop manner at clock-driven intervals, we observe a decrease in the final distance to the target location. Furthermore, we combine the primitives into a closed loop hybrid control strategy using tactile and proprioceptive information to leverage the advantages of both primitives without being overly disruptive. In doing so, we achieve a 10-fold increase in success rate above the baseline control strategy and significantly improve completion times as compared to the primitives alone or a naive combination of them.

In this blog post, I will explain how machine learning fits into the broader landscape of data and computer science. This means understanding how machine learning interrelates with parent fields and sister disciplines. This is important, as these are the terms you will see time and again when searching for relevant study materials and hear mentioned ad nauseam in machine learning books. Relevant disciplines can also be difficult and confusing to tell apart at first glance, such as'machine learning' and'data mining.' The lineage of machine learning can be understood by first examining its forefathers.