HUME lets robots generate, plan over, and actively verify object-centric hypotheses from foundation models so incomplete symbolic models become usable for open-world household tasks.
Embodied question answering
3 Pith papers cite this work. Polarity classification is still indexing.
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Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.
Robo-Cortex proposes a self-evolving embodied navigation agent using dual-grain cognitive memory and autonomous knowledge induction from trajectories, reporting SPL gains on IGNav, AR, AEQA and preliminary real-robot tests.
citing papers explorer
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning
HUME lets robots generate, plan over, and actively verify object-centric hypotheses from foundation models so incomplete symbolic models become usable for open-world household tasks.
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How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study
Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.
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Robo-Cortex: A Self-Evolving Embodied Agent via Dual-Grain Cognitive Memory and Autonomous Knowledge Induction
Robo-Cortex proposes a self-evolving embodied navigation agent using dual-grain cognitive memory and autonomous knowledge induction from trajectories, reporting SPL gains on IGNav, AR, AEQA and preliminary real-robot tests.