Hallucinative Topological Memory for Zero-Shot Visual Planning

• URL: http://arxiv.org/abs/2002.12336v1
• Date: Thu, 27 Feb 2020 18:54:42 GMT
• Title: Hallucinative Topological Memory for Zero-Shot Visual Planning
• Authors: Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar
• Abstract summary: In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline. Most previous works on VP approached the problem by planning in a learned latent space, resulting in low-quality visual plans. Here, we propose a simple VP method that plans directly in image space and displays competitive performance.
• Score: 86.20780756832502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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