  We propose a method that actively estimates contact location between a
grasped rigid object and its environment and uses this as input to a
peg-in-hole insertion policy. An estimation model and an active tactile
feedback controller work collaboratively to estimate the external contacts
accurately. The controller helps the estimation model get a better estimate by
regulating a consistent contact mode. The better estimation makes it easier for
the controller to regulate the contact. We then train an object-agnostic
insertion policy that learns to use the series of contact estimates to guide
the insertion of an unseen peg into a hole. In contrast with previous works
that learn a policy directly from tactile signals, since this policy is in
contact configuration space, it can be learned directly in simulation. Lastly,
we demonstrate and evaluate the active extrinsic contact line estimation and
the trained insertion policy together in a real experiment. We show that the
proposed method inserts various-shaped test objects with higher success rates
and fewer insertion attempts than previous work with end-to-end approaches. See
supplementary video and results at
https://sites.google.com/view/active-extrinsic-contact.
