Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos (2505.18899v1)

Abstract: Imitation from videos often fails when expert demonstrations and learner environments exhibit domain shifts, such as discrepancies in lighting, color, or texture. While visual randomization partially addresses this problem by augmenting training data, it remains computationally intensive and inherently reactive, struggling with unseen scenarios. We propose a different approach: instead of randomizing appearances, we eliminate their influence entirely by rethinking the sensory representation itself. Inspired by biological vision systems that prioritize temporal transients (e.g., retinal ganglion cells) and by recent sensor advancements, we introduce event-inspired perception for visually robust imitation. Our method converts standard RGB videos into a sparse, event-based representation that encodes temporal intensity gradients, discarding static appearance features. This biologically grounded approach disentangles motion dynamics from visual style, enabling robust visual imitation from observations even in the presence of visual mismatches between expert and agent environments. By training policies on event streams, we achieve invariance to appearance-based distractors without requiring computationally expensive and environment-specific data augmentation techniques. Experiments across the DeepMind Control Suite and the Adroit platform for dynamic dexterous manipulation show the efficacy of our method. Our code is publicly available at Eb-LAIfO.

• The paper introduces an event-inspired perception method that transforms RGB videos into event-based format, enhancing visual imitation learning robustness against domain shifts by focusing on motion dynamics.
• Experiments across simulated benchmarks demonstrate that this event-based approach achieves near expert-level performance and adaptability under significant visual disparities and disturbances.
• This method offers computational efficiency by discarding static features and has theoretical implications, aligning with biological vision and suggesting new directions for biomimetic AI research.

Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos

The paper under review addresses a critical limitation in the domain of Visual Imitation from Observations (V-IfO)—specifically, the challenges posed by domain shifts between expert demonstrations and learner environments in imitation from videos. These shifts manifest as variations in lighting, color, or texture, often degrading the performance of imitation learning systems. Conventional approaches, such as domain randomization or visual data augmentation, have proven both computationally intensive and insufficiently robust against unseen scenarios. Instead of randomizing visual appearances to mitigate these issues, this paper proposes a fundamental shift in strategy by altering the sensory representation itself—a concept inspired by biological vision systems and event-based cameras.

Key Contributions

1. Event-Inspired Perception Mechanism: The paper introduces an event-based perception method that transforms continuous RGB videos into a discrete, event-based format which focuses solely on temporal intensity gradients. This representation aims to remove static appearance features like lighting and color, focusing instead on motion dynamics that are more invariant to domain shifts.
2. Implementation and Evaluation: The proposed method is integrated into a visually robust imitation learning framework and tested across diverse simulated benchmarks, including the DeepMind Control Suite and the Adroit platform. These experiments successfully demonstrate the ability of the event-based approach to achieve high performance despite significant appearance mismatches between expert and learner domains.
3. Computational Efficiency: Unlike conventional methods, this approach eliminates the need for computationally expensive data augmentation or domain-specific randomization techniques by discarding non-essential visual features directly through its perceptual transformation.

Strong Numerical Results and Claims

In controlled experimental setups, the event-inspired perception approach shows marked improvements over state-of-the-art adversarial imitation learning techniques. Specifically, experiments showed that the proposed method can almost achieve expert-level performance even under extreme domain disparities. Additionally, the approach adapts seamlessly to unexpected visual disturbances, reflecting its robustness and practical usability in variable real-world conditions.

Theoretical Implications

The research not only offers practical advancements but also redefines the theoretical underpinnings of the imitation learning domain. By reimagining the input feature space through event-based transformations, the paper challenges traditional reliance on large-scale training data and stratifies the input by prioritizing dynamic over static features. This conceptual shift aligns more closely with biological sensory systems, hinting at new directions for biomimetic AI research.

Future Directions

Looking forward, the adoption of real-world event cameras promises to further enhance the applicability of this research. Future explorations might seek to refine the accuracy of event stream conversions, improve noise resilience, and expand the framework to handle additional complexities like depth information. Additionally, theoretical extensions could explore the intersection of event-based representations and reinforcement learning to develop more sophisticated models for operating in dynamic, real-life environments.

The findings of this paper have broad implications for advancing the robustness and applicability of machine learning systems tasked with imitation learning from videos, especially in domains requiring adaptability to unforeseen domain changes. This work lays the groundwork for future explorations into event-based vision systems and their potential to revolutionize machine learning paradigms.