Joint Distribution Between Rankings (leader and Metric Based) and Metric Values

Edson Oliveira,Eduardo Fernandes,Igor Steinmacher,Alessandro Garcia,Marco Cristo,Tayana Conte

openalex（2019）

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：本文提出了交互式机器人车队学习框架Fleet-DAgger，通过可扩展的人类监督来优化机器人车队的控制策略，并引入了新的性能评价指标ROHE（Return on Human Effort）。

【方法】：作者通过构建Interactive Fleet Learning（IFL）设置，将多个机器人与多个人类监督者之间的交互学习形式化，并提出了基于ROHE的Fleet-DAgger算法。

【实验】：在模拟环境中，作者将Fleet-DAgger算法与4个基线算法进行了比较，使用100个机器人进行了测试。此外，还进行了实体实验，使用4个ABB YuMi机器人臂和2个远程人类操作者进行块推动任务。实验结果表明，Fleet-DAgger算法相比基线可达到高达8.8倍的ROHE提升。实验使用了Isaac Gym环境，这是一个GPU加速的开源模拟环境。