Frontier Models for Robotic Manipulation: Rethinking Interaction and Scaffolding

Frontier foundation models can reason over images, write programs, and interpret execution feedback. We ask how these capabilities should be exposed to a robot, comparing two interaction paradigms: execution-time decision making, where the model chooses each next action, and executable-policy synthesis, where the model writes a persistent robot program and revises it from execution feedback. In our instantiation of the latter, GPT-6 works in a coding session with a minimal robot interface and repeatedly writes, executes, observes, and revises a policy. We evaluate on LIBERO, LIBERO-Pro, and the RoboCasa365 Target50 subset, then vary the refinement budget and the amount of human-designed scaffolding with the model, interface, and environment fixed. We conclude the following: First, stronger models improve both interaction paradigms: GPT-6 achieves 100% success on LIBERO, 95.0% on LIBERO-Pro, and 88.0% on RoboCasa365 Target50, including 75.0% on unseen composite tasks. Second, interaction remains important even for frontier models: increasing the refinement budget from one to eight turns raises LIBERO-Pro success from 45% to 94%, showing that executable-policy synthesis remains an iterative rather than one-shot generation problem. Third, additional imposed structure provides no consistent benefit: predefined skills, abstractions, and heuristic scaffolding do not reliably outperform a minimal interface and can restrict how the model discovers and revises solutions. Together, these results suggest that realizing frontier-model capabilities in robotics depends not only on model strength, but also on interaction design: effective interfaces should provide sufficient opportunities for execution feedback and revision without unnecessarily constraining the model's reasoning process.