Adaptive World Models for Space Robotics: Implicit Representations Grounded in Semantics and Physics

Robots operating in uncertain space environments must be able to construct and continuously refine their understanding of their environment to make effective decisions and complete tasks. Real-time decision-making poses two key challenges: (1) developing models of the environment, or world models, that capture object semantics and dynamical properties, and (2) adapting these world models from new observations to guide robotic actions and reduce environmental uncertainty, especially in previously uncharacterized environments. This research aims to develop adaptive world models with object dynamics and semantics within a 3D implicit neural representation. These implicit representations utilize continuous functions to represent 3D objects. We aim to leverage deep learning, differentiable rendering, and 3D implicit representation techniques to develop these world models. Building on efficient meta-learning frameworks that learn how to learn, these models will be adaptive, continually refining their understanding of the environment based on new observations. Enhanced world modeling capabilities can significantly benefit NASA's goals in various space exploration objectives. For instance, robots with adaptive world models could explore lunar lava tubes more effectively, differentiating between objects such as rocks and mineral deposits to aid exploration and data gathering. Moreover, these world models are crucial for robots repairing space infrastructure, like satellites, by understanding individual object parts and their dynamical properties. Overall, adaptive world models with semantic and dynamical properties have tremendous potential to advance NASA's goals in space exploration by enabling robots to make more informed decisions, execute tasks more effectively, and adapt to unexpected situations with greater autonomy.

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