In the vacuum of space, where gravity is negligible and human presence is limited, robotic systems must perform complex assembly tasks with precision and efficiency. Traditional robotic manipulation relies on rigid programming and predefined object models—an approach that struggles in the dynamic, unpredictable environments of space stations or lunar habitats. Affordance-based manipulation presents a paradigm shift, enabling robots to interpret and leverage object functionalities intuitively.
The term "affordance" originates from ecological psychology, coined by James J. Gibson in 1979. In robotics, affordances refer to the actionable properties of objects—how they can be manipulated or interacted with based on their shape, material, and context. For example:
By recognizing these properties autonomously, robots can adapt to novel objects without explicit programming—a critical capability in space missions where payload constraints limit the variety of pre-mission training data.
Microgravity introduces challenges absent in terrestrial robotics:
Affordance-based systems mitigate these issues by enabling real-time adaptation. For instance, a robot could detect that a drifting panel affords "anchoring" via built-in mounting points, then adjust its approach vector to compensate for momentum transfer.
NASA’s Astrobee free-flying robots aboard the International Space Station (ISS) demonstrate primitive affordance use. While not fully autonomous, their navigation systems interpret handrails and experiment racks as "graspable" for station-keeping. Future iterations could expand this to interpret modular components like the ISS’s STP-H6 payload rack, whose standardized interfaces afford alignment and locking mechanisms.
Implementing affordance-based manipulation requires multi-modal perception and machine learning:
Two dominant approaches exist:
In microgravity, self-supervised methods excel due to their adaptability. A robot might discover that a tool’s magnetic base affords "stick-to-surface" only after testing it against a metallic habitat wall.
The Artemis program’s planned Lunar Gateway requires autonomous assembly of modular habitats. Affordance-aware robots could:
Harvard’s TERMES project showed how simple robots could build complex structures using block affordances (e.g., "climbable" steps). Scaling this to lunar conditions would require adding vacuum-rated adhesives or electromagnets as alternative "attachment" affordances.
Despite progress, key hurdles remain:
Ongoing research at JPL’s NeBula program aims to address these through edge-computing and probabilistic affordance models.
Affordance-based systems could revolutionize other orbital tasks:
Apollo 13’s CO₂ scrubber fix—using available canisters, hoses, and tape—was essentially human affordance reasoning. Modern robots must replicate such adaptability autonomously.
The transition from scripted manipulation to affordance-based robotics marks a leap toward true autonomy in space construction. By treating objects not as rigid CAD models but as collections of potential interactions, robots can assemble habitats, repair infrastructure, and even repurpose materials with human-like versatility—essential for sustainable off-world presence.