  This letter describes an approach to achieve well-known Chinese cooking art
stir-fry on a bimanual robot system. Stir-fry requires a sequence of highly
dynamic coordinated movements, which is usually difficult to learn for a chef,
let alone transfer to robots. In this letter, we define a canonical stir-fry
movement, and then propose a decoupled framework for learning this deformable
object manipulation from human demonstration. First, the dual arms of the robot
are decoupled into different roles (a leader and follower) and learned with
classical and neural network-based methods separately, then the bimanual task
is transformed into a coordination problem. To obtain general bimanual
coordination, we secondly propose a Graph and Transformer based model --
Structured-Transformer, to capture the spatio-temporal relationship between
dual-arm movements. Finally, by adding visual feedback of content deformation,
our framework can adjust the movements automatically to achieve the desired
stir-fry effect. We verify the framework by a simulator and deploy it on a real
bimanual Panda robot system. The experimental results validate our framework
can realize the bimanual robot stir-fry motion and have the potential to extend
to other deformable objects with bimanual coordination.
