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Video prediction models combined with planning algorithms have shown promisein enabling robots to learn to perform many vision-based tasks through onlyself-supervision, reaching novel goals in cluttered scenes with unseen objects.However, due to the compounding uncertainty in long horizon video predictionand poor scalability of sampling-based planning optimizers, one significantlimitation of these approaches is the ability to plan over long horizons toreach distant goals. To that end, we propose a framework for subgoal generationand planning, hierarchical visual foresight (HVF), which generates subgoalimages conditioned on a goal image, and uses them for planning. The subgoalimages are directly optimized to decompose the task into easy to plan segments,and as a result, we observe that the method naturally identifies semanticallymeaningful states as subgoals. Across three out of four simulated vision-basedmanipulation tasks, we find that our method achieves nearly a 200% performanceimprovement over planning without subgoals and model-free RL approaches.Further, our experiments illustrate that our approach extends to real,cluttered visual scenes. Project page:https://sites.google.com/stanford.edu/hvf

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation