ALAS disentangles environment and self-state streams via bio-inspired modules to deliver 23% higher subtask success and 29% better execution efficiency on long-horizon HSI tasks.
Steve-eye: Equipping llm-based embodied agents with visual perception in open worlds.arXiv preprint arXiv:2310.13255,
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EvolvingAgent autonomously completes long-horizon tasks via a closed-loop planner-controller-reflector system with continual world model updates, reporting 111.74% higher success rates than baselines in Minecraft and human-level Atari performance.
citing papers explorer
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ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement
ALAS disentangles environment and self-state streams via bio-inspired modules to deliver 23% higher subtask success and 29% better execution efficiency on long-horizon HSI tasks.
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EvolvingAgent: Curriculum Self-evolving Agent with Continual World Model for Long-Horizon Tasks
EvolvingAgent autonomously completes long-horizon tasks via a closed-loop planner-controller-reflector system with continual world model updates, reporting 111.74% higher success rates than baselines in Minecraft and human-level Atari performance.