Learning Plannable Representations with Causal InfoGAN (1807.09341v1)

Abstract: In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans -- a plausible sequence of observations that transition a dynamical system from its current configuration to a desired goal state, which can later be used as a reference trajectory for control. We focus on systems with high-dimensional observations, such as images, and propose an approach that naturally combines representation learning and planning. Our framework learns a generative model of sequential observations, where the generative process is induced by a transition in a low-dimensional planning model, and an additional noise. By maximizing the mutual information between the generated observations and the transition in the planning model, we obtain a low-dimensional representation that best explains the causal nature of the data. We structure the planning model to be compatible with efficient planning algorithms, and we propose several such models based on either discrete or continuous states. Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations. We demonstrate our method on imagining plausible visual plans of rope manipulation.

• The paper's main contribution is the introduction of Causal InfoGAN, which generates structured latent spaces for goal-directed planning from high-dimensional observations.
• It employs mutual information maximization to align state transitions with underlying causal dynamics, enhancing prediction and feasibility of action sequences.
• The framework outperforms baseline models in visually challenging tasks like rope manipulation, demonstrating potential for practical robotics and simulation applications.

Insights on "Learning Plannable Representations with Causal InfoGAN"

The paper under discussion elaborates on a novel approach for integrating deep generative models and structured planning within high-dimensional observation domains, particularly focusing on visual observation sequences like robot manipulation of objects. The authors propose a framework titled "Causal InfoGAN" which leverages the strengths of deep learning paradigms and traditional AI planning methodologies to enable goal-directed planning through learned representations.

Core Contributions

This work primarily introduces a new generative model, Causal InfoGAN, designed to produce plannable representations of dynamical systems from high-dimensional inputs such as images. The central idea is to construct a latent space that is both expressive and structured, thereby allowing effective planning. This synthesis has the capability to address challenges in developing trajectories that transition a system from a start state to a goal state using learned representations grounded in real observations.

Key highlights of the approach include:

• Generative Modeling with Structured Latent Space: The authors employ a GAN (Generative Adversarial Network) model where the generator is conditioned not only on noise but also on state transitions in a low-dimensional planning system. This strategy not only provides a compact representation of the high-dimensional input space but also aligns the model with efficient graph-based planning algorithms.
• Mutual Information Maximization: The work utilizes the concept of maximizing mutual information between observations and state transitions. This is critical for learning representations that capture the underlying causal dynamics essential for predicting plausible sequences of observations that lead to the completion of a task.
• Integration of Discrete and Continuous Models: By exploring both discrete one-hot and binary representations as well as continuous state representations, the framework demonstrates versatility in handling different types of problems, paving the way for future rigidity and flexibility enhancements in task planning.

Numerical Insights and Claims

The paper presents robust evaluations demonstrating the framework's efficacy in generating plausible plans in visually challenging dynamical systems. For instance, Causal InfoGAN is tested in a rope manipulation task where it outperforms baseline methods such as InfoGAN and DCGAN in producing more coherent and feasible visual plan sequences. The discriminator from GAN, adapted into a classifier, validates the feasibility of transitions, proving the generated paths align well with the dynamic system's realities.

Implications and Future Directions

The integration of GAN-based models with planning paradigms yields significant implications for AI research and applications:

• Practical Implications: The approach offers practical solutions to problems requiring long-term reasoning and planning in domains like robotic manipulation, automated scenario generation in simulation, and autonomous exploration tasks.
• Theoretical Developments: From a theoretical standpoint, the proposal bridges the gap between deep learning capabilities in representation learning and traditional AI methods in planning, opening avenues for further exploration into hierarchical task learning and execution.
• Scalability and Complexity: The authors provide a foundation for experimenting with more complex, multi-object environments and scaling the approach to broader classes of visual planning problems.

Causal InfoGAN marks a meaningful step towards holistic AI systems capable of understanding and interacting with the physical world through learned knowledge. Future work could extend this framework to account for multi-agent environments and incorporate reinforcement learning signals to drive exploration and skill acquisition in more dynamic and responsive ways.