Self-Supervised Correspondence in Visuomotor Policy Learning (1909.06933v1)

Abstract: In this paper we explore using self-supervised correspondence for improving the generalization performance and sample efficiency of visuomotor policy learning. Prior work has primarily used approaches such as autoencoding, pose-based losses, and end-to-end policy optimization in order to train the visual portion of visuomotor policies. We instead propose an approach using self-supervised dense visual correspondence training, and show this enables visuomotor policy learning with surprisingly high generalization performance with modest amounts of data: using imitation learning, we demonstrate extensive hardware validation on challenging manipulation tasks with as few as 50 demonstrations. Our learned policies can generalize across classes of objects, react to deformable object configurations, and manipulate textureless symmetrical objects in a variety of backgrounds, all with closed-loop, real-time vision-based policies. Simulated imitation learning experiments suggest that correspondence training offers sample complexity and generalization benefits compared to autoencoding and end-to-end training.

• The paper introduces a self-supervised dense visual correspondence method that reduces reliance on human labels.
• The methodology combines imitation learning with sparse demonstrations (50-150 samples) to achieve robust performance in both simulation and real-world tasks.
• Empirical results demonstrate high generalization with metrics close to ground truth, such as a 97% success rate in a 'Push sugar box' task.

Self-Supervised Correspondence in Visuomotor Policy Learning: A Technical Overview

The paper "Self-Supervised Correspondence in Visuomotor Policy Learning" presents a methodology for enhancing the efficiency and generalization of visuomotor policy learning through self-supervised dense visual correspondence. This research diverges from traditional methods such as autoencoding, pose-based losses, and end-to-end policy optimization by leveraging dense visual correspondence in a self-supervised manner to foster improved learning in visuomotor policies.

Key Contributions and Methodology

1. Self-Supervised Learning Paradigm: The central thesis of this paper is the proposal of a self-supervised learning framework for visuomotor tasks using dense visual correspondence. This method does not require additional human labels, making it scalable and adaptable to various conditions and tasks.
2. Visuomotor Policy Training: The paper outlines a novel approach that integrates imitation learning with self-supervised visual correspondence training to achieve high generalization with a minimal amount of data. Specifically, they demonstrate this capability through hardware validation in manipulation tasks using only 50 to 150 demonstrations.
3. Comparison with Benchmark Methods: The authors conducted detailed simulation experiments to compare the proposed method against other established approaches like end-to-end training and autoencoding. They report significant benefits in sample efficiency and generalization, where their method approaches the performance achieved through access to ground truth state information.
4. Application to Hardware and Real-World Environments: Empirical validation is a noteworthy aspect of this work, where the trained policies have been tested in real-world environments, handling tasks with deformable objects and tasks requiring generalization across object classes with considerable success.

Numerical Results and Achievements

The paper provides robust numerical insights that underscore the efficacy of self-supervised correspondence training. In controlled simulation environments, the proposed system effectively generalized across various tasks with an impressive success rate. For instance, in tasks involving translation and rotation, the algorithms using the newly proposed dense descriptor approach showed performance metrics on par with those utilizing ground truth positions.

In hardware experiments, the methodology maintained reliability even under physically challenging constraints such as disturbances and varying visual conditions. For instance, the "Push sugar box" task indicated over 97% success rate despite physical disturbances, highlighting the system's robustness.

Implications and Future Directions

The implications of this work are manifold. By effectively utilizing dense visual correspondence, the researchers illustrate a pathway toward scalable, efficient visuomotor policy learning without the need for extensive human supervision. Theoretically, this framework aligns with the growing trend of self-supervised learning where models leverage inherent structure in data to learn useful task representations.

Practically, such advancements have profound effects on robotic manipulation in unstructured environments. As robots engage in more complex tasks, the need for adaptable learning paradigms grows, and the presented method provides a foundational step toward realizing these capabilities.

Looking ahead, innovative breakthroughs could emerge from extending this framework to scenarios involving multiple instance representations or hybridizing object recognition with spatial task decomposition. Such developments could further close the gap between trained robotic systems and the dynamic complexity of real-world environments.

Conclusion

This paper contributes significantly to the field by introducing efficient methodologies for training visuomotor policies. The self-supervised approach not only reduces data dependency but also exhibits strong adaptability and scalability across diverse tasks. This research effectively opens avenues for deeper exploration into self-supervised learning mechanisms, with potential ramifications across AI and robotics in terms of autonomy and learning efficiency.