Estimating a user''s intention is central to close human-robot cooperation. In this paper, the problem of performing intention recognition with tree-structured Dynamic Bayesian Networks for large environments with many features is addressed. The proposed approach reduces the computational complexity of inference O(bs) for tree-structured measurement models with an average branching factor b and tree height s to O(b̃s), where b̃ ≪ b. The key idea is to switch between a finite set of reduced system and measurement models in order to restrict inference to the most important features. A model predictive approach to online switching between the reduced models is proposed that exploits an upper bound of the distances of the reduced models to the full model. The effectiveness of the proposed algorithm is validated in the intention recognition for a humanoid robot using a telepresent household scenario.
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机译：估计用户的意图对于密切人机合作至关重要。在本文中，解决了在具有许多功能的大型环境中使用树结构动态贝叶斯网络执行意图识别的问题。对于平均分支因子为b且树的高度为O（b̃ s ）的树状结构测量模型，该方法降低了推理O（b s ）的计算复杂度。 ，其中b̃ b。关键思想是在有限的一组简化系统和测量模型之间切换，以将推理限制在最重要的特征上。提出了一种在简化模型之间在线切换的模型预测方法，该方法利用了简化模型到完整模型之间距离的上限。拟议的算法的有效性在使用远程演示家庭场景的类人机器人的意图识别中得到了验证。
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