A recent salon convened nearly 200 experts in embodied AI to discuss the challenges of moving robots from labs to the real world, focusing on data collection and model training. Participants highlighted that current data collection methods are inefficient and costly, with a significant portion of collected data being unusable for training. The discussion also touched upon the need for better data alignment, standardized evaluation benchmarks, and the potential of pre-training paradigms similar to those used in large language models. AI
IMPACT Highlights the critical need for better data collection and alignment in embodied AI, suggesting current methods are inefficient and may hinder scaling.
RANK_REASON The cluster discusses a salon and research findings on embodied AI data and models, including benchmark designs and pre-training strategies.
- Ant Group
- Beijing Humanoid Robot Innovation Center
- Cooperas Technology
- ICRA 2026
- Kuaifu 4 Pro
- Leju Robot
- LingBot
- LingBot-Depth
- LingBot-VLA
- Pi 0.5
- Shanghai Jiao Tong University
- Vienna
- Zhiyuan Institute of Artificial Intelligence
- QbitAI
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