  Most object manipulation strategies for robots are based on the assumption
that the object is rigid (i.e., with fixed geometry) and the goal's details
have been fully specified (e.g., the exact target pose). However, there are
many tasks that involve spatial relations in human environments where these
conditions may be hard to satisfy, e.g., bending and placing a cable inside an
unknown container. To develop advanced robotic manipulation capabilities in
unstructured environments that avoid these assumptions, we propose a novel
long-horizon framework that exploits contrastive planning in finding promising
collaborative actions. Using simulation data collected by random actions, we
learn an embedding model in a contrastive manner that encodes the
spatio-temporal information from successful experiences, which facilitates the
subgoal planning through clustering in the latent space. Based on the keypoint
correspondence-based action parameterization, we design a leader-follower
control scheme for the collaboration between dual arms. All models of our
policy are automatically trained in simulation and can be directly transferred
to real-world environments. To validate the proposed framework, we conduct a
detailed experimental study on a complex scenario subject to environmental and
reachability constraints in both simulation and real environments.
