AI RESEARCH
EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks
arXiv CS.CV
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ArXi:2604.09535v1 Announce Type: new Large foundation models have made significant advances in embodied intelligence, enabling synthesis and reasoning over egocentric input for household tasks. However, VLM-based auto-labeling is often noisy because the primary data sources lack accurate human action labels, chain-of-thought (CoT), and spatial annotations; these errors are amplified during long-horizon spatial instruction following. These issues stem from insufficient coverage of minute-long, daily household planning tasks and from inaccurate spatial grounding.