机器人遇新物体时，主动采集数据以学会未来识别它。
Embodied Active Learning of Generative Sensor-Object Models
- 基于潜在不确定性主动选择采集数据，提升学习效率。
- 在真实机械臂上验证，可识别未知数量的新物体。
- 适合对机器人自主感知与学习感兴趣的开发者。
当机器人遇到未知物体时，应如何响应——收集何种数据，才能在未来识别该物体？本文提出一种方法，用于学习未知数量新物体的图像特征。通过基于潜在不确定性的主动覆盖策略，结合遍历稳定性与PAC-Bayes理论，将统计保证从变分自编码器（VAE）扩展至具身智能体。在真实机械臂硬件上验证了该方法，同时在仿真环境中也完成了实现。算法与仿真代码已开源，详见 http://sites.google.com/u.northwestern.edu/embodied-learning-hardware。
原文摘要 · Abstract (English)
When a robot encounters a novel object, how should it respond$\unicode{x2014}$what data should it collect$\unicode{x2014}$so that it can find the object in the future? In this work, we present a method for learning image features of an unknown number of novel objects. To do this, we use active coverage with respect to latent uncertainties of the novel descriptions. We apply ergodic stability and PAC-Bayes theory to extend statistical guarantees for VAEs to embodied agents. We demonstrate the method in hardware with a robotic arm; the pipeline is also implemented in a simulated environment. Algorithms and simulation are available open source, see http://sites.google.com/u.northwestern.edu/embodied-learning-hardware .
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