TRASS: Time Reversal as Self-Supervision

A longstanding challenge in robot learning for manipulation tasks has been the ability to generalize to varying initial conditions, diverse objects, and changing objectives. Learning based approaches have shown promise in producing robust policies, but require heavy supervision and large number of environment interactions, especially from visual inputs. We propose a novel self-supervision technique that uses time-reversal to provide high level supervision to reach goals. In particular, we introduce the time-reversal model (TRM), a self-supervised model which explores outward from a set of goal states and learns to predict these trajectories in reverse. This provides a high level plan towards goals, allowing us to learn complex manipulation tasks with no demonstrations or exploration at test time. We test our method on the domain of assembly, specifically the mating of tetris-style block pairs. Using our method operating atop visual model predictive control, we are able to assemble tetris blocks on a KuKa IIWA-7 using only uncalibrated RGB camera input, and generalize to unseen block pairs. Project's-page: https://sites.google.com/view/time-reversal.