SCOPE is a self-adaptive symbolic planning framework that refines plans and evolves symbolic world models via simulator feedback and distilled knowledge to improve long-horizon planning in open-ended embodied environments.
Grounding classical task planners via vision-language models.arXiv preprint arXiv:2304.08587,
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OpenVLA achieves 16.5% higher task success than the 55B RT-2-X model across 29 tasks with 7x fewer parameters while enabling effective fine-tuning and quantization without performance loss.
Survey organizing VLM-based social robot navigation into reasoning, planning, and bridging components with a proposed roadmap for hybrid deployable systems.
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OpenVLA: An Open-Source Vision-Language-Action Model
OpenVLA achieves 16.5% higher task success than the 55B RT-2-X model across 29 tasks with 7x fewer parameters while enabling effective fine-tuning and quantization without performance loss.