Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation. Despite its potential as a pre-training technique for image-based reinforcement learning (RL) tasks in terms of sample efficiency, systematic generalization, and reasoning, the advantages of OCR in RL haven't been systematically examined.
Our work delves into the effectiveness of OCR pre-training for image-based RL tasks, driven by empirical experiments and analyses through a series of questions and answers. We introduced a simple object-centric visual RL benchmark for a comprehensive evaluation. Our findings contribute valuable insights into OCR pre-training for RL, outlining its benefits and potential limitations in particular scenarios.
Questions we aimed to answer include:
"Does OCR pre-training improve performance on object-centric tasks?"
"Can OCR pre-training help with out-of-distribution generalization?"
The research examines critical aspects of incorporating OCR pre-training in RL, such as performance in visually complex environments and the selection of an appropriate pooling layer for aggregating object representations.
Our benchmark is meticulously crafted to assess the proficiency and boundaries of various RL models. It focuses on essential capabilities, such as object recognition, interaction, and relational reasoning.
Obj. Goal
Obj. Interaction
Obj. Comparison
Prop. Comparison
Obj. Reach
Our benchmark comprises of diverse tasks categorized into five major types:
Object Goal Task: Navigate to a designated object (blue box) in the environment.
Object Interaction Task: Push the blue box in the middle of the observation to another blue box at the left bottom of the scene.
Object Comparison Task: Find and go to the distinct object among the objects.
Property Comparison Task: Find and go to the object with the unique property.
Object Reach Task: Object Goal Tasks on visually more complex environments.
These tasks span a broad spectrum of difficulty and complexity, providing a comprehensive evaluation of the models against diverse challenges.
An overview of the models we have evaluated using our Object-Centric Visual Reinforcement Learning Benchmark.
We present our experimental results and analysis as a series of questions and answers, each probing a different aspect of OCR pre-training for RL.
OCR pre-training does not always provide better performance for every object-centric task, but for relational reasoning tasks, it demonstrates better performance when compared to other diversely trained representations.
OCR pre-training may not always improve sample efficiency for every object-centric task, but it does enhance sample efficiency for tasks where the relationship between objects is important.
For the unseen number of objects, GT and CNN showed comparable generalization performance to OCR pre-training, while for the unseen type of objects, OCR pre-training showed more robust generalization performance than baselines.
The agent utilizing SLATE demonstrated superior sample efficiency and converged success rate compared to the other methods. Although this task does not explicitly require reasoning among the objects, it is still crucial for the agent to learn to avoid touching the distractor objects before the target object.
From the figure on Question 1, It is clear that the SLATE model performs the best in terms of overall performance on the tasks evaluated in this study.
The use of the MLP pooling layer resulted in inferior performance on all tasks, with a complete failure to solve the interaction and comparison tasks.
OCR pre-training is efficient for comparison tasks, while it is comparable to end-to-end learning methods for the Object Goal task. However, for the Object Interaction task, the gap between end-to-end learned CNN and SLATE is much larger than that observed in Question 2, due to the computational demands of SLATE.
The binding problem in neural networks refers to the difficulty in representing multiple objects as distinct entities when they are encoded into a single vector representation. OCRs, on the other hand, provide a scalable solution to this problem, as they can represent multiple objects independently.
The results show that as the number of objects increases, the performance of both models decreases. However, the performance degradation of the CNN model is much greater than that of the SLATE model. The results show that with fewer objects in the environment, both VAE and SLATE performed better, with the difference being more pronounced for the VAE model.
To address this question, we redesigned the comparison tasks to require more complex reasoning. Specifically, we extended the comparison tasks to include three shapes or three colors, and evaluated the hardest condition in our benchmark, which consisted of one of four colors, three shapes, and two sizes.
The results indicate that as the task becomes harder, learning is slower, and all models failed to solve the Property Comparison task within 2 million steps when the object could have one of two sizes. However, OCR pre-training still exhibited comparable performance to ground truth states for all tasks, including the Property Comparison tasks.
We investigate the possibility of OCR pre-training on an out-of-distribution environment to test its efficacy.
Our results indicate that the model achieves a success rate of over or approximately 90\% on the tasks, despite the objects being previously unseen. While this performance is lower than that achieved in the in-distribution environment, it is still superior to the baselines. The model is capable of accurately segmenting objects, despite not being able to reconstruct the images perfectly. These findings suggest that OCR pre-training can be advantageous for out-of-distribution tasks if the model is capable of segmenting objects.
In this study, we aimed to investigate the correlation between standard OCR evaluation metrics, such as segmentation quality, reconstruction loss, and property prediction accuracy, and RL performance.
The results indicate that while the FG-ARI showed a negative correlation with RL performance across all tasks, the correlation between MSE, property prediction accuracy, and RL performance was inconsistent, with positive correlation observed for some tasks and negative correlation for others. From these results, we hypothesize that when performance in segmentation or reconstruction is good enough, such as in the case of SLATE, the correlation with RL performance is weaker.
We found more specific conditions to satisfy the hypotheses through this empirical investigation. OCR pre-training does not always provide sample efficiency but is efficient for relational reasoning tasks. OCR pre-training is not always better than other methods for out-of-distribution tasks also, but it shows better generalization performance for unseen objects when compared with GT.
We chose those visually simple scenes to ensure the downstream RL performance is not affected by poor segmentation quality. It allows us to probe more specific aspects of the reinforcement learning task to assess where OCR pre-training is most beneficial. In order to investigate the case where segmentation quality is not perfect, we also ran experiments on the robotics Object Reaching Task, which we discuss in Question 4. Investigating OCR in more complex and realistic environments is a promising direction for future work, especially as unsupervised OCR models continue to improve.
Lastly, we hope our benchmark can help evaluate OCR models in the context of agent learning!
You can discover additional details about the experiments and other intriguing experimental findings in our paper. We hope you find them enjoyable!