Zerui Li
Publications
Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation
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 70.7$\pm$3.5% success (opus-5, mean over three runs), and fable-5 reaches 78% at maximum effort. When a trained waypoint tool is exposed alongside primitives as an optional capability, the hybrid fable-5 agent reaches 76.7$\pm$0.6% 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.
SpatialAnt: Autonomous Zero-Shot Robot Navigation via Active Scene Reconstruction and Visual Anticipation
Vision-and-Language Navigation (VLN) has recently benefited from Multimodal Large Language Models (MLLMs), enabling zero-shot navigation. While recent exploration-based zero-shot methods have shown promising results by leveraging global scene priors, they rely on high-quality human-crafted scene reconstructions, which are impractical for real-world robot deployment. When encountering an unseen environment, a robot should build its own priors through pre-exploration. However, these self-built reconstructions are inevitably incomplete and noisy, which severely degrade methods that depend on high-quality scene reconstructions. To address these issues, we propose SpatialAnt, a zero-shot navigation framework designed to bridge the gap between imperfect self-reconstructions and robust execution. SpatialAnt introduces a physical grounding strategy to recover the absolute metric scale for monocular-based reconstructions. Furthermore, rather than treating the noisy self-reconstructed scenes as absolute spatial references, we propose a novel visual anticipation mechanism. This mechanism leverages the noisy point clouds to render future observations, enabling the agent to perform counterfactual reasoning and prune paths that contradict human instructions. Extensive experiments in both simulated and real-world environments demonstrate that SpatialAnt significantly outperforms existing zero-shot methods. We achieve a 66% Success Rate (SR) on R2R-CE and 50.8% SR on RxR-CE benchmarks. Physical deployment on a Hello Robot further confirms the efficiency and efficacy of our framework, achieving a 52% SR in challenging real-world settings.