Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics
Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics
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DOI：
10.1145/3359614
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发表时间：
2020-01-01
影响因子：
5.1
通讯作者：
Argall, Brenna
中科院分区：
文献类型：
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作者：
Jain, Siddarth;Argall, Brenna
在共享自治中有效的人机协作需要对人类合作伙伴的意图进行推理。为了提供有意义的帮助，自主性必须首先正确地预测或推断人类合作者的预期目标。在这项工作中，我们提出了一个数学公式的意图推理辅助遥操作共享自治下。我们的递归贝叶斯过滤方法模型，并融合多个非语言的观察概率的原因，没有明确的沟通，用户的预期目标。除了上下文的观察，我们的模型，并将人类代理的行为作为目标导向的行动与可调整的合理性，告知意图识别。此外，我们引入了用户定制优化这种可调的合理性，以实现用户个性化。我们验证了我们的方法与人类受试者的研究，评估各种目标场景和任务下的意图推理性能。重要的是，研究是使用多个控制界面，通常可供用户在辅助领域，不同的连续性和维度发出的控制信号。分析了控制接口限制对意图推断的影响。研究结果表明，我们的方法在许多情况下优于现有的解决方案，在辅助遥操作的意图推理，否则执行可扩展性。我们的研究结果表明，概率建模和纳入人类代理行为的目标导向的行动，可调的合理性模型是用户自定义的好处。结果进一步表明，潜在的意图推理方法直接影响共享自治性能，控制接口的限制。
Effective human-robot collaboration in shared autonomy requires reasoning about the intentions of the human partner. To provide meaningful assistance, the autonomy has to first correctly predict, or infer, the intended goal of the human collaborator. In this work, we present a mathematical formulation for intent inference during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user without explicit communication. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform intent recognition. Furthermore, we introduce a user-customized optimization of this adjustable rationality to achieve user personalization. We validate our approach with a human subjects study that evaluates intent inference performance under a variety of goal scenarios and tasks. Importantly, the studies are performed using multiple control interfaces that are typically available to users in the assistive domain, which differ in the continuity and dimensionality of the issued control signals. The implications of the control interface limitations on intent inference are analyzed. The study results show that our approach in many scenarios outperforms existing solutions for intent inference in assistive teleoperation and otherwise performs comparably. Our findings demonstrate the benefit of probabilistic modeling and the incorporation of human agent behavior as goal-directed actions where the adjustable rationality model is user customized. Results further show that the underlying intent inference approach directly affects shared autonomy performance, as do control interface limitations.