Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation

arXiv:2607.26148v1 Announce Type: new Abstract: Autonomous embodied agents must sustain a long decision-making loop that involves perceiving, acting, verifying, and self-correcting over many steps. Current systems sustain this loop through task-specific workflows or embodied policies. We study a third form, agentic embodied control, in which a general-purpose agent holds the loop itself. Using zero-shot navigation as a controlled testbed, we evaluate three software-engineering agent harnesses given only a monocular RGB camera and discrete actions. Under this strictly minimal condition, replicated default-effort configurations reach 70.7pm3.5% success (opus-5, mean over three runs), and fable-5 reaches 78% at maximum effort. When a trained waypoint tool is exposed alongside primitives as an optional capability, the hybrid fable-5 agent reaches 76.7pm0.6% at default effort, using half the environment steps and less than one quarter of the wall time of the maximum-effort primitive run. Controlled interventions show that capability is primarily model-centered: model choice strongly changes success, harness effects are descriptive, and a forced waypoint interface helps weaker models but can hinder stronger ones. Performance nevertheless falls sharply on longer-horizon tasks, while latency and context growth limit sustained operation. These results show that agentic control is already competitive in zero-shot navigation and that models, harnesses, and interfaces offer complementary paths toward autonomous embodied agents.

Source: arXiv cs.RO | 2026-07-30