Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency

Understand

At the heart of many robotics problems is the challenge of learning correspondences across domains.

• For instance, imitation learning requires obtaining correspondence between humans and robots; sim-to-real requires correspondence between physics simulators and the real world; transfer learning requires correspondences between different robotics environments.
• This paper aims to learn correspondence across domains differing in representation (vision vs.
• internal state), physics parameters (mass and friction), and morphology (number of limbs).