  We present placing via picking (PvP), a method to autonomously collect
real-world demonstrations for a family of placing tasks in which objects must
be manipulated to specific, contact-constrained locations. With PvP, we
approach the collection of robotic object placement demonstrations by reversing
the grasping process and exploiting the inherent symmetry of the pick and place
problems. Specifically, we obtain placing demonstrations from a set of grasp
sequences of objects initially located at their target placement locations. Our
system can collect hundreds of demonstrations in contact-constrained
environments without human intervention using two modules: compliant control
for grasping and tactile regrasping. We train a policy directly from visual
observations through behavioural cloning, using the autonomously-collected
demonstrations. By doing so, the policy can generalize to object placement
scenarios outside of the training environment without privileged information
(e.g., placing a plate picked up from a table). We validate our approach in
home robot scenarios that include dishwasher loading and table setting. Our
approach yields robotic placing policies that outperform policies trained with
kinesthetic teaching, both in terms of success rate and data efficiency, while
requiring no human supervision.
