POLAR organizes prior interactions into a multimodal knowledge graph with semantic and episodic memory to improve personalized embodied task execution across multiple MLLM backbones.
Empowering large language models on robotic manipulation with affordance prompting
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Digital twin representations from vision foundation models enable LLM-based planning for robust peg transfer and gauze retrieval on the dVRK surgical platform with claimed generalizability.
citing papers explorer
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Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions
POLAR organizes prior interactions into a multimodal knowledge graph with semantic and episodic memory to improve personalized embodied task execution across multiple MLLM backbones.
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Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models
Digital twin representations from vision foundation models enable LLM-based planning for robust peg transfer and gauze retrieval on the dVRK surgical platform with claimed generalizability.
- As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture