Video prediction models combined with planning algorithms have shown promise
in enabling robots to learn to perform many vision-based tasks through only
self-supervision, reaching novel goals in cluttered scenes with unseen objects.
However, due to the compounding uncertainty in long horizon video prediction
and poor scalability of sampling-based planning optimizers, one significant
limitation of these approaches is the ability to plan over long horizons to
reach distant goals. To that end, we propose a framework for subgoal generation
and planning, hierarchical visual foresight (HVF), which generates subgoal
images conditioned on a goal image, and uses them for planning. The subgoal
images are directly optimized to decompose the task into easy to plan segments,
and as a result, we observe that the method naturally identifies semantically
meaningful states as subgoals. Across three out of four simulated vision-based
manipulation tasks, we find that our method achieves nearly a 200% performance
improvement over planning without subgoals and model-free RL approaches.
Further, our experiments illustrate that our approach extends to real,
cluttered visual scenes. Project page:
this https URL

通过子目标生成和规划的分层视觉预见（HVF）框架，可以将任务分解成易于规划的片段，并自然地识别语义上有意义的状态作为子目标，在三个模拟的基于视觉的操作任务中，我们的方法实现了近 200% 的性能提升。