Name
Title | ||
|---|---|---|
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation |
Abstract | ||
|---|---|---|
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 more than 20% absolute performance improvement over planning without subgoals and model-free RL approaches. Further, our experiments illustrate that our approach extends to real, cluttered visual scenes. |
Year | Venue | Keywords |
|---|---|---|
2020 | ICLR | video prediction, reinforcement learning, planning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
30 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S Nair | 1 | 21 | 3.86 |
| Chelsea Finn | 2 | 819 | 57.17 |