  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). Importantly, correspondences are learned using unpaired and
randomly collected data from the two domains. We propose \textit{dynamics
cycles} that align dynamic robot behavior across two domains using a
cycle-consistency constraint. Once this correspondence is found, we can
directly transfer the policy trained on one domain to the other, without
needing any additional fine-tuning on the second domain. We perform experiments
across a variety of problem domains, both in simulation and on real robot. Our
framework is able to align uncalibrated monocular video of a real robot arm to
dynamic state-action trajectories of a simulated arm without paired data. Video
demonstrations of our results are available at:
https://sjtuzq.github.io/cycle_dynamics.html .
