The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion
The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion
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DOI：
10.1007/s00422-012-0526-2
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发表时间：
2013-02-01
影响因子：
1.9
通讯作者：
Butz, Martin V.
中科院分区：
文献类型：
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作者：
Ehrenfeld, Stephan;Butz, Martin V.
人类在运动计划和执行方面表现出令人钦佩的能力。他们可以在各种情况下执行复杂的任务，非常有效地利用可用的感官信息。身体模型和连续的身体状态估计似乎是实现这种能力的必要条件。我们引入了模块化模态框架（MMF）模型，该模型保持高度分布式、模块化的身体模型，随着时间的推移不断更新，模块化的身体状态估计。模块化是根据模态框架来实现的，也就是说，在特定的参考框架和特定的身体部位中，感官模态是实现的。我们在三维空间的模拟九自由度臂上评估了MMF的性能。结果表明，尽管传感器和电机噪声较高，MMF仍能保持准确的身体状态估计。此外，通过比较不同模态帧中可用的感觉信息，MMF可以实时识别错误的感觉测量。在不久的将来，应该追求在轻型机器人控制方面的应用。此外，神经编码可以通过引入神经种群编码和学习技术来增强MMF。最后，通过利用可用的冗余状态表示来实现更灵巧的目标导向行为。
Humans show admirable capabilities in movement planning and execution. They can perform complex tasks in various contexts, using the available sensory information very effectively. Body models and continuous body state estimations appear necessary to realize such capabilities. We introduce the Modular Modality Frame (MMF) model, which maintains a highly distributed, modularized body model continuously updating, modularized probabilistic body state estimations over time. Modularization is realized with respect to modality frames, that is, sensory modalities in particular frames of reference and with respect to particular body parts. We evaluate MMF performance on a simulated, nine degree of freedom arm in 3D space. The results show that MMF is able to maintain accurate body state estimations despite high sensor and motor noise. Moreover, by comparing the sensory information available in different modality frames, MMF can identify faulty sensory measurements on the fly. In the near future, applications to lightweight robot control should be pursued. Moreover, MMF may be enhanced with neural encodings by introducing neural population codes and learning techniques. Finally, more dexterous goal-directed behavior should be realized by exploiting the available redundant state representations.