Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation (2003.08974v1)

Abstract: We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects. Planning is performed in a low-dimensional latent state space that embeds images. We define and implement a Latent Space Roadmap (LSR) which is a graph-based structure that globally captures the latent system dynamics. Our framework consists of two main components: a Visual Foresight Module (VFM) that generates a visual plan as a sequence of images, and an Action Proposal Network (APN) that predicts the actions between them. We show the effectiveness of the method on a simulated box stacking task as well as a T-shirt folding task performed with a real robot.

• The paper introduces a Latent Space Roadmap (LSR) framework using a VAE and Action Proposal Network for efficient visual action planning in high-dimensional spaces like robot manipulation.
• Evaluation showed 100% success rate in simulated box stacking and high action prediction accuracy in real-world T-shirt folding, demonstrating the framework's effectiveness for both rigid and deformable objects.
• This research offers theoretical insights into latent space planning and practical applications for developing scalable robot task planning systems, especially for complex deformable object manipulation.

Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation

The paper presents a novel framework for visual action planning within the field of robotics, particularly focused on tasks involving high-dimensional state spaces such as the manipulation of both deformable and rigid objects. This framework introduces a Latent Space Roadmap (LSR) to efficiently plan tasks in a low-dimensional latent space that represents a condensed version of the high-dimensional image input space.

Summary of Contributions

The authors propose a two-component system:

1. Visual Foresight Module (VFM): This component employs a Variational Autoencoder (VAE) to create a latent space representing the potential states of the system. The latent space is structured via an augmented VAE loss function incorporating an action term to ensure valid state representations and dynamics. The LSR built within this space serves as a graph-based structure capturing the global transitions required for manipulation tasks, providing a robust framework for planning without dense data coverage or extensive system dynamics modeling.
2. Action Proposal Network (APN): Once a sequence of latent states (a latent plan) is determined, the APN predicts the sequence of actions to achieve these transitions. It is trained on the enlarged latent state dataset to ensure that the proposed actions correspond to feasible manipulations within the given latent framework.

Numerical Results and Evaluation

The framework was validated through two experimental setups:

• Simulated Box Stacking Task: This domain provides a controlled environment to evaluate the efficacy of the LSR and VAE-structured latent space. The paper reports that the LSR, combined with the structured latent space, successfully generates valid plans with 100% success when using the $L_1$ metric. This demonstrates the effectiveness of using appropriately chosen metrics in the latent space to improve planning accuracy.
• Real-World T-shirt Folding: This task tests the system's capability to handle highly deformable objects. Results show high APN prediction accuracy and a significant achievement in completing the folding task, albeit with performance variability across different metrics used to structure the latent space. The framework's adaptability and success in such a complex task underline its potential in real-world robotics applications.

Theoretical and Practical Implications

The research offers two significant contributions:

• Theoretical Insight: The definition and implementation of the LSR introduce a novel perspective on path planning in latent spaces, enhancing our understanding of latent space dynamics in complex systems. The methodology circumvents the need for exhaustive analytical modeling or dense data coverage, emphasizing the importance of data-driven low-dimensional representations in complex robotic systems.
• Practical Application: By demonstrating the LSR's effectiveness in both simulated and real-world environments, this framework opens new avenues for efficient robot task planning. The capacity to handle deformable object manipulation without explicit physical modeling is particularly noteworthy, suggesting potential for broader application in industrial automation and household robotics.

Future Directions

The work suggests further exploration into diverse areas such as integrating reinforcement learning for more dynamic planning adjustments and expanding the scope of the LSR framework to include motion prediction and handling more complex task sequences. By continuing to refine the latent space structuring techniques and exploring additional metric impacts, the framework could be enhanced to address a wider array of robotic tasks with increased robustness and efficiency.

In summary, the introduction of a Latent Space Roadmap represents a meaningful advancement in the field of robotic planning, offering a scalable and flexible approach to handling high-dimensional and dynamic manipulation tasks.