Vid2Param: Online system identification from video for robotics applications
Vid2Param: Online system identification from video for robotics applications
复制标题
Vid2Param：机器人应用视频的在线系统识别
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Ramamoorthy
中科院分区：
文献类型：
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作者：
Martin Asenov;Michael Burke;Daniel Angelov;Todor Davchev;Kartic Subr;S. Ramamoorthy
在动态环境中执行任务的机器人将大大受益于了解潜在的环境运动，以便对未来进行预测，并综合使用这种归纳偏差的有效控制策略。因此，在线系统识别是一个强大的自主代理的基本要求。当动态涉及多个模式（由于对象之间的接触或交互）时，以及当系统识别必须直接从丰富的感官流（如视频）进行时，则传统的系统识别方法可能不太适合。我们提出了一种方法，其中快速参数估计模型可以无缝地结合一个经常性的变分自动编码器。我们的基于物理的循环变分自动编码器模型包括一个额外的损失，强制符合基于物理的动力学模型的结构。这使得所得到的模型能够对诸如位置、速度、恢复、空气阻力和系统的其他物理属性之类的参数进行编码。该模型可以完全在仿真中进行训练，以端到端的方式进行域随机化，以执行在线系统识别和感兴趣参数的概率前向预测。我们对现有的系统识别方法进行基准测试，并证明Vid2Param在识别的速度和准确性方面优于基线，并且还以未来轨迹分布的形式提供不确定性量化。此外，我们说明了这在物理实验中的实用程序，其中PR2机器人与速度约束的手臂必须拦截弹跳球，估计这个球的物理参数直接从视频跟踪球被释放后。
Robots performing tasks in dynamic environments would benefit greatly from understanding the underlying environment motion, in order to make future predictions and to synthesize effective control policies that use this inductive bias. Online system identification is therefore a fundamental requirement for robust autonomous agents. When the dynamics involves multiple modes (due to contacts or interactions between objects), and when system identification must proceed directly from a rich sensory stream such as video, then traditional methods for system identification may not be well suited. We propose an approach wherein fast parameter estimation with a model can be seamlessly combined with a recurrent variational autoencoder. Our Physics-based recurrent variational autoencoder model includes an additional loss that enforces conformity with the structure of a physically based dynamics model. This enables the resulting model to encode parameters such as position, velocity, restitution, air drag and other physical properties of the system. The model can be trained entirely in simulation, in an end-to-end manner with domain randomization, to perform online system identification, and probabilistic forward predictions of parameters of interest. We benchmark against existing system identification methods and demonstrate that Vid2Param outperforms the baselines in terms of speed and accuracy of identification, and also provides uncertainty quantification in the form of a distribution over future trajectories. Furthermore, we illustrate the utility of this in physical experiments wherein a PR2 robot with velocity constrained arm must intercept a bouncing ball, by estimating the physical parameters of this ball directly from the video trace after the ball is released.
DOI：
10.48550/arxiv.1903.01267
发表时间：
2019
期刊：
arXiv e-prints
影响因子：
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作者：
Angelov Daniel
通讯作者：
Angelov Daniel
从示范中学习的可解释的潜在空间
DOI：
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发表时间：
2018
期刊：
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影响因子：
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作者：
Hristov YS
通讯作者：
Hristov YS