2019/12/06 by Nam Hee Kim, Kim, Nam Hee, Zhaoming Xie +3 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music Technology and Sound Studies #Robotic Locomotion and Control #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1912.03015
openalex publication_date 2019/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many dynamical systems exhibit similar structure, as often captured by hand-designed simplified models that can be used for analysis and control. We develop a method for learning to correspond pairs of dynamical systems via a learned latent dynamical system. Given trajectory data from two dynamical systems, we learn a shared latent state space and a shared latent dynamics model, along with an encoder-decoder pair for each of the original systems. With the learned correspondences in place, we can use a simulation of one system to produce an imagined motion of its counterpart. We can also simulate in the learned latent dynamics and synthesize the motions of both corresponding systems, as a form of bisimulation. We demonstrate the approach using pairs of controlled bipedal walkers, as well as by pairing a walker with a controlled pendulum.