Autonomous Learning of Assembly Tasks from the Corresponding Disassembly Tasks

An assembly task is in many cases just a reverse
execution of the corresponding disassembly task. During the
assembly, the object being assembled passes consecutively from
state to state until completed, and the set of possible movements
becomes more and more constrained. Based on the observation
that autonomous learning of physically constrained tasks can
be advantageous, we use information obtained during learning
of disassembly in assembly. For autonomous learning of a
disassembly policy we propose to use hierarchical reinforcement
learning, where learning is decomposed into a highlevel
decision-making and underlying lower-level intelligent
compliant controller, which exploits the natural motion in
a constrained environment. During the reverse execution of
disassembly policy, the motion is further optimized by means
of an iterative learning controller. The proposed approach was
verified on two challenging tasks - a maze learning problem and
autonomous learning of inserting a car bulb into the casing.