Interoceptive robustness through environment-mediated morphological development (1804.02257v2)

Abstract: Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies.

• The paper investigates how environment-mediated morphological development driven by interoceptive stress or pressure enhances robustness and diversity in soft robots evolved using CPPNs.
• Key findings show stress-responsive robots are more robust to material variations, while pressure-responsive robots exhibit greater morphological diversity and less canalization.
• The research suggests integrating developmental plasticity improves robotic resilience for deployment in unpredictable environments and offers insights for developing adaptive AI neural architectures.

Interoceptive Robustness through Environment-Mediated Morphological Development

The paper "Interoceptive Robustness through Environment-Mediated Morphological Development" presented at GECCO '18 explores a novel dimension in robotic evolution by integrating morphological development in response to interoceptive stimuli, specifically focusing on engineering stress. Unlike traditional approaches that rely on fixed body plans and neural architectures optimized through evolutionary or learning algorithms, this research investigates how the geometry, control systems, and material properties of soft robots can evolve to enhance robustness against material defects and variable conditions.

Overview and Methods

The researchers employed a scenario wherein robots composed of voxels with heterogeneous stiffness levels respond developmentally to mechanical stress during their lifetimes. This development-induced plasticity in material stiffness is hypothesized to enable increased robustness and adaptability. To test this hypothesis, the authors implemented a series of evolutionary trials using a physics engine, Voxelyze, which simulated soft-matter interactions in a 3D environment. The robots were subject to three different developmental variants: no developmental change, response to stress, and response to pressure.

The robots' physiologies were encoded using CPPNs (Compositional Pattern Producing Networks), which determined voxel presence, congenital stiffness, developmental changes, and actuation patterns. The evolutionary process, guided by an Age-Fitness-Pareto Optimization algorithm, generated robots that were optimized for locomotion, with a focus on maintaining fitness across developmental and environmental perturbations.

Key Findings

• Evolvability and Diversity: The paper found no significant variance in fitness among the different developmental treatments. However, pressure-adaptive robots exhibited greater morphological diversity than stress-adaptive counterparts, suggesting that pressure-response promotes evolutionary divergence in geometric structures.
• Robustness: Stress-responsive robots demonstrated higher robustness to altered stiffness distributions than non-adaptive and pressure-adaptive robots. This implies that stress-responsive development buffers against extreme deviations from expected material properties, a trait not observed in pressure-responsive robots.
• Canalization: Stress-adaptive development was correlated with greater canalization, indicating more uniform and less reactive stiffness changes across the robot's body. This was contrasted with the localized and variable developmental reactions seen in pressure-adaptive robots.

Implications and Future Directions

This research contributes to the understanding of developmental feedback mechanisms in robotic systems, offering insights on how environmental and interoceptive stimuli can influence the evolution of robust robotic architectures. The distinction between stress and pressure as developmental stimuli highlights the need for targeted responses that can exploit specific load signatures to drive robustness without sacrificing diversity.

From a practical perspective, the integration of interoceptive development increases the potential for the deployment of resilient robots in unpredictable environments. The implications extend to the development of neural architectures in AI, suggesting avenues for adaptability through structural modifications in response to internal processing signals.

Future work could explore a broader spectrum of interoceptive stimuli and their potential to facilitate adaptive strategies in both morphological and neural domains. Furthermore, the principles of environment-mediated development could inform the design of hybrid systems, where dynamic morphologies are paired with adaptive neural networks, enhancing performance and resilience in complex, real-world applications.

In summary, this paper underscores the potential of embedding developmental plasticity into robotic systems to achieve robust performance, laying a groundwork for future explorations into adaptive morphologies and their applications in AI and robotics.