  In visual planning (VP), an agent learns to plan goal-directed behavior from
observations of a dynamical system obtained offline, e.g., images obtained from
self-supervised robot interaction. Most previous works on VP approached the
problem by planning in a learned latent space, resulting in low-quality visual
plans, and difficult training algorithms. Here, instead, we propose a simple VP
method that plans directly in image space and displays competitive performance.
We build on the semi-parametric topological memory (SPTM) method: image samples
are treated as nodes in a graph, the graph connectivity is learned from image
sequence data, and planning can be performed using conventional graph search
methods. We propose two modifications on SPTM. First, we train an energy-based
graph connectivity function using contrastive predictive coding that admits
stable training. Second, to allow zero-shot planning in new domains, we learn a
conditional VAE model that generates images given a context of the domain, and
use these hallucinated samples for building the connectivity graph and
planning. We show that this simple approach significantly outperform the
state-of-the-art VP methods, in terms of both plan interpretability and success
rate when using the plan to guide a trajectory-following controller.
Interestingly, our method can pick up non-trivial visual properties of objects,
such as their geometry, and account for it in the plans.
