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This work proposes StageGuard, an agentic distillation framework for accurate and efficient stage-transition decisions, which combines teacher-model reasoning with demonstration trajectories to generate structured explanations of subtask completion and policy switching.
A single-layer action head on a 30-layer video backbone enables Faster-WAM to achieve a 3.2x speedup in inference latency while maintaining competitive performance and robust generalization.
Future visual cues can dramatically enhance navigation performance, even when not accessible during actual deployment.
RoboHarness achieves remarkable improvements in long-horizon planning by seamlessly orchestrating diverse robot policies without retraining, even in uncertain environments.
Overcoming perceptual uncertainty in vision-language navigation is now possible by explicitly modeling geometric, semantic, and appearance uncertainty with a novel Uncertainty-Aware Gaussian Map.
Web-scale video pretraining lets robots handle real-world chaos better than vision-language models trained on curated robotics datasets.
