Unsupervised Learning of Object Keypoints for Perception and Control (1906.11883v2)

Abstract: The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates. Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods. Furthermore, consistent long-term tracking enables two notable results in control domains -- (1) using the keypoint co-ordinates and corresponding image features as inputs enables highly sample-efficient reinforcement learning; (2) learning to explore by controlling keypoint locations drastically reduces the search space, enabling deep exploration (leading to states unreachable through random action exploration) without any extrinsic rewards.

• The paper introduces the Transporter architecture that accurately learns unsupervised keypoints for robust object tracking in dynamic environments.
• It significantly improves sample efficiency in reinforcement learning by using keypoint-derived state abstractions that outperform traditional methods.
• The approach enables efficient exploration by reducing the action search space, ultimately enhancing control capabilities in RL tasks.

Unsupervised Learning of Object Keypoints for Perception and Control: An Overview

The paper "Unsupervised Learning of Object Keypoints for Perception and Control" presents a novel approach to object representation within the context of computer vision, with a primary focus on applications in reinforcement learning (RL). This research introduces the Transporter, a neural network architecture designed to identify and track geometric object representations through keypoints derived in an unsupervised manner from raw video data. The approach aims to address the shortcomings of previous methods that either fail to factorize geometry or do not support accurate long-term tracking of object parts—a crucial requirement in RL contexts.

Key Contributions

The Transporter model leverages a feature transport mechanism, where image features are transferred between video frames using a keypoint bottleneck. This mechanism supports the extraction of fine-grained geometric features and object parts, enabling robust object tracking over extended time horizons. Importantly, the representation learned encompasses precise control capabilities, a feature that traditional vision methods in RL have struggled to achieve.

The research achieves the following:

1. Enhanced Keypoint Detection: The method showcases the ability to learn more accurate and consistent keypoints compared to existing unsupervised methods. Evaluations demonstrate superior tracking performance across standard RL domains such as Atari games and robotic manipulators.
2. Sample-Efficient Reinforcement Learning: By utilizing keypoint coordinates in conjunction with object-specific image features as inputs, the architecture significantly improves the data efficiency of RL, outperforming existing model-free and model-based approaches, particularly in the context of Atari games.
3. Exploration in RL: A novel approach to exploration is introduced, where controlling keypoint positions provides a reduced search space for action selection. This method allows for more efficient exploration of environments, reaching states typically inaccessible through random exploration strategies.

Methodology and Evaluation

The Transporter model comprises several core components, including a convolutional network for spatial feature extraction, a keypoint network for 2D coordinate estimation, and a refinement network tasked with reconstructing transported feature maps into images. This methodology is evaluated against state-of-the-art unsupervised keypoint detection methods through metrics focused on the precision and recall of keypoint trajectories. The evaluations illustrate the Transporter's effectiveness in providing temporally consistent keypoints that remain robust across frames, even when subjected to complex dynamic transformations.

In applying these keypoints to reinforcement learning tasks, the paper demonstrates how they serve as an efficient state abstraction in neural fitted Q-learning frameworks. Pre-training on diverse datasets further accelerates learning compared to traditional methods. The role of keypoints extends to learning task-agnostic options that significantly enhance exploration efficiency, allowing for a substantial reduction in the search space within the keypoint-driven action space.

Implications and Future Prospects

The implications of this research are significant both in theoretical and practical contexts. The Transporter's ability to perform accurate object tracking in an unsupervised manner provides a foundation for developing RL systems that require less task-specific data and can generalize across tasks. The insights gained could inform the development of more adaptable robotic systems and AI agents capable of performing complex sensorimotor tasks with minimal supervision.

Future research directions could explore scaling the model to more diverse and complex datasets or integrating explicit reasoning about potential background motion as outlined by the authors. The introduction of temporal consistency and the ability to disentangle object dynamics and affordances may further enhance RL capabilities, allowing for more autonomous, adaptable AI systems.

In conclusion, the Transporter architecture presents a significant advance in the field of unsupervised visual representation learning, offering a promising approach to enabling efficient perception and control in reinforcement learning contexts. The adaptability and efficiency demonstrated by the model pave the way for broader applications and more sophisticated AI systems in the future.