Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies (1812.11971v3)

Abstract: How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. delivering a package)? We study this question by integrating a generic perceptual skill set (e.g. a distance estimator, an edge detector, etc.) within a reinforcement learning framework--see Figure 1. This skill set (hereafter mid-level perception) provides the policy with a more processed state of the world compared to raw images. We find that using a mid-level perception confers significant advantages over training end-to-end from scratch (i.e. not leveraging priors) in navigation-oriented tasks. Agents are able to generalize to situations where the from-scratch approach fails and training becomes significantly more sample efficient. However, we show that realizing these gains requires careful selection of the mid-level perceptual skills. Therefore, we refine our findings into an efficient max-coverage feature set that can be adopted in lieu of raw images. We perform our study in completely separate buildings for training and testing and compare against visually blind baseline policies and state-of-the-art feature learning methods.

• The paper shows that mid-level visual representations markedly improve sample efficiency and policy generalization in RL-based visuomotor tasks.
• It employs perceptually curated features such as depth estimation and edge detection to simplify decision-making in dynamic environments.
• Experimental results in realistic virtual settings validate that structured visual priors outperform traditional end-to-end learning approaches.

An Expert Analysis of the Integration of Mid-Level Visual Representations in Visuomotor Policy Learning

This paper provides a thorough investigation into the role of mid-level visual representations within reinforcement learning (RL) frameworks, specifically targeting visuomotor tasks. The paper advocates for leveraging mid-level perceptual skills—such as depth estimation and edge detection—rather than training end-to-end from raw pixel data, emphasizing their advantages in generalization and sample efficiency for robotic and navigation-oriented tasks.

Key Findings and Methodology

The authors demonstrate that incorporating mid-level visual representations into RL agents offers significant benefits in both learning speed and generalization capabilities, especially when agents are exposed to unseen test environments. These mid-level features act as perceptual priors, tailored to simplify the decision-making processes for the agent by providing a processed view of the environment. Essentially, this paper refines the current understanding of how perceptual biases can aid in overcoming the typical hurdles faced by RL-based approaches, such as high sample complexity and limited cross-environment generalization.

A central claim of the paper is that relying exclusively on raw sensor data is suboptimal when certain perceptual structure about the world can be encoded via mid-level features. However, the paper notes that a naive selection of mid-level features will not automatically confer these advantages. It points out the necessity of a nuanced curation of these features to support various downstream tasks effectively.

Experimental Setup and Results

The experiments were carried out in realistic virtual environments using the Gibson environment, chosen for its perceptual resemblance to real-world settings. The paper considers multiple tasks such as navigation to a target object, visual exploration, and local planning. Each of these tasks is designed to test different aspects of the policy learning process and the utility of mid-level representations therein.

Concretely, the paper found that feature-based agents consistently demonstrated superior learning speed compared to those trained from scratch. The tabula rasa approach, though elegant, failed to generalize across environments with significant visual differences, a limitation not observed in agents leveraging mid-level visual priors.

Statistical analyses were employed to verify these claims. It was shown that the performance distributions between mid-level features and the end-to-end trained models were significantly distinct, corroborating the quantitative benefits of incorporating perceptual biases into RL frameworks.

Implications and Future Directions

The implications of this paper are notable for both theoretical exploration and practical deployment of AI systems. Practically, the findings suggest that the use of task-specific perceptual biases should be favored in scenarios where data acquisition is costly or impractical. Theoretically, the research challenges the conventional end-to-end paradigm dominant in deep learning and RL, proposing a middle ground where structured priors enhance learning efficacy.

For future research, exploration into the expansion of the mid-level feature set is warranted. Moreover, investigating the dynamic updating of these perceptual modules in a life-long learning context can be particularly beneficial. Another area ripe for exploration is the potential crossover and adaptability of these methods to non-locomotive tasks that require complex decision-making processes.

Conclusion

This paper offers a compelling argument for redefining how visual information is utilized within RL for visuomotor tasks. By focusing on discerning mid-level features already available in the environmental cues, the paper not only presents empirical evidence for improved outcomes but also lays the groundwork for further academic pursuit in enhancing RL paradigms for real-world applications. This shift towards perception-aware agents might prompt re-evaluation of some of the fundamental assumptions underlying current AI systems.