Embodied AI Comparison Hub

Reference Surfaces

Top Papers

• Seed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation(8.0)

  Seed2Scale is a self-evolving data engine for embodied AI that leverages small and large model synergy to generate high-quality training data, enabling significant performance improvements with minimal initial data.

• SVLL: Staged Vision-Language Learning for Physically Grounded Embodied Task Planning(8.0)

  SVLL is a novel framework for robust, physically-grounded embodied task planning that outperforms existing models in real-world applications.

• V-CAGE: Context-Aware Generation and Verification for Scalable Long-Horizon Embodied Tasks(7.0)

  V-CAGE generates semantically robust manipulation datasets to enhance long-horizon task automation in complex environments.

• Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning(6.0)

  R&B-EnCoRe refines embodied reasoning for Vision-Language-Action models using self-supervised learning, achieving notable performance gains.

• pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI(6.0)

  Develop a personalized federated learning framework optimizing vision-language navigation models for improved privacy and performance in diverse environments.

• On the Strengths and Weaknesses of Data for Open-set Embodied Assistance(5.0)

  Develop an AI model for adaptive user assistance through synthetic datasets and fine-tuning techniques.