Make {pi}0.5 Learn to Call Astra for Reliable Robot Manipulation

arXiv:2610.14117v1 Announce Type: new Abstract: GPT-6 Astra provides robust embodied reasoning, but frequent external queries increase inference time and token costs. We equip a frozen pi_{0.5} policy with a lightweight CALL head that learns to predict when assistance is necessary. CALL reuses features from the standard policy forward pass and operates locally without consulting external models. Astra is activated solely for critical recovery events and returns control to pi_{0.5} upon stabilization. Evaluated across 40 LIBERO tasks (200 episodes), our system achieves a 97.5% success rate, outperforming the 95.5% baseline of pure pi_{0.5}. In the Object, Goal, and Spatial suites, Astra executes only 5.49% of environment steps, consuming approximately 5,027 tokens per test episode. This selective intervention preserves the fast ordinary execution of pi_{0.5} while seamlessly integrating Astra's reasoning on demand. Our project page is available at https://kykgeek.github.io/Pi05_CALL_Astra/.

Source: arXiv cs.RO | 2026-10-14