通过动作识别提升人机协作中的装配状态推理鲁棒性。
Robust Assembly State Reasoning from Action Recognition for Human-Robot Collaboration
- 结合动作识别结果，用逻辑、隐马尔可夫和神经网络方法追踪装配状态。
- 在低变化任务中神经网络与隐马尔可夫模型表现更优，高复杂场景下逻辑方法更稳健。
- 考虑动作持续时间建模对重复动作场景至关重要，尤其无额外传感时。
人类动作识别（HAR）常用于人机协作（HRC）研究，以理解已完成的动作并推断协作任务的状态。然而，从HAR准确追踪装配状态尚未被充分探索，在真实场景中也非易事。本研究系统地比较了五种基于动作识别输入的装配状态追踪方法：逻辑规则、隐马尔可夫模型（HMM）和神经网络（NN）方法。实验使用两个不同数据集，分别测试模拟噪声输入与真实HAR模型输出。结果表明，最优方法不具普适性，不同方法在不同场景下表现各异。在低变异性任务中，NN与HMM方法表现良好；而在复杂场景中，逻辑方法更具鲁棒性。对于重复动作任务，若无额外传感，建模预期动作持续时间极为关键。
原文摘要 · Abstract (English)
Human Action Recognition (HAR) is frequently investigated in Human-Robot Collaboration (HRC) research to understand what actions have been performed and hence the state of a collaborative task. Accurately tracking an assembly state from HAR is however not fully investigated, and in realistic scenarios is not a trivial task. This research systematically investigates and compares methods for tracking assembly state using action recognition inputs. Investigations using two diverse datasets and five state tracking approaches, including logic-based, Hidden Markov Model (HMM), and neural network (NN) methods, show that optimal approaches are not uniform across different tasks and that different methods fail under different circumstances. Testing is performed using both simulated inputs with varying noise levels and realistic inputs from a HAR model. Results show NN and HMM methods can perform well in tasks with limited variability, but for other scenarios logic-based approaches can be more robust. Methods which model expected action duration are also important for tasks with repeated actions where no additional sensing is provided.
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