  Insertion operations are a critical element of most robotic assembly
operation, and peg-in-hole (PiH) insertion is one of the most widely studied
tasks in the industrial and academic manipulation communities. PiH insertion is
in fact an entire class of problems, where the complexity of the problem can
depend on the type of misalignment and contact formation during an insertion
attempt. In this paper, we present the design and analysis of adaptive
compliance controllers which can be used in insertion-type assembly tasks,
including learning-based compliance controllers which can be used for insertion
problems in the presence of uncertainty in the goal location during robotic
assembly. We first present the design of compliance controllers which can
ensure safe operation of the robot by limiting experienced contact forces
during contact formation. Consequently, we present analysis of the force
signature obtained during the contact formation to learn the corrective action
needed to perform insertion. Finally, we use the proposed compliance
controllers and learned models to design a policy that can successfully perform
insertion in novel test conditions with almost perfect success rate. We
validate the proposed approach on a physical robotic test-bed using a 6-DoF
manipulator arm.
