LUNARMBENCH - Benchmarking Robotic Manipulation for Lunar Surface Logistics & Infrastructure

NASA’s Moon Base operations will require robotic systems to manipulate logistics items, payloads, cables, connectors, and infrastructure interfaces before crew arrival, during uncrewed periods, and alongside astronauts. At the same time, robotic manipulation is advancing rapidly through physical AI, learning-based control, improved simulation, and robot foundation models, but existing benchmarks remain largely terrestrial and do not evaluate the lunar-relevant tasks, interfaces, environmental stressors, or operational constraints NASA must understand. This CIF will establish LunArmBench, an Ames-led V0 benchmark and evaluation framework for lunar surface robotic manipulation. The project will translate Moon Base Autonomous Systems & Robotics capability targets into simulation-based benchmark tasks, focused on cable/connector mate-demate, cable handling/routing, logistics payload handling, and payload/interface alignment. The benchmark will define task protocols, manipulation primitives, scoring metrics, lunar-relevant stressors, and baseline evaluation methods. Using NVIDIA Isaac Sim/Isaac Lab and a low-cost bimanual manipulation platform, the team will produce initial simulation assets, benchmark specifications, baseline results, and a hardware validation roadmap. LunArmBench will provide Ames and NASA with reusable evaluation infrastructure to guide physical AI development, inform robot-compatible interface design, and support follow-on Moon Base robotics maturation.

The goal of LunArmBench is to establish an Ames-led V0 benchmark and evaluation framework for lunar surface robotic manipulation. LunArmBench will translate Moon Base manipulation needs into representative task definitions, manipulation primitives, simulation assets, scoring metrics, and baseline evaluations. The initial focus will be on three task families: cable/connector mate-demate and cable routing; logistics payload handling and staging; and payload/interface placement and alignment. Our hypothesis is that a compact, mission-grounded benchmark can provide actionable evidence about which robotic manipulation approaches, interfaces, and autonomy methods are most promising for lunar surface operations, while positioning Ames to lead a reusable evaluation capability for Moon Base physical AI.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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