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Human-robot collaboration (HRC) has become an integral element of many industries, including manufacturing. A fundamental requirement for safe HRC is to understand and predict human intentions and trajectories, especially when humans and robots operate in close proximity. However, predicting both human intention and trajectory components simultaneously remains a research gap. In this paper, we have developed a multi-task learning (MTL) framework designed for HRC, which processes motion data from both human and robot trajectories. The first task predicts human trajectories, focusing on reconstructing the motion sequences. The second task employs supervised learning, specifically a Support Vector Machine (SVM), to predict human intention based on the latent representation. In addition, an unsupervised learning method, Hidden Markov Model (HMM), is utilized for human intention prediction that offers a different approach to decoding the latent features. The proposed framework uses MTL to understand human behavior in complex manufacturing environments. The novelty of the work includes the use of a latent representation to capture temporal dynamics in human motion sequences and a comparative analysis of various encoder architectures. We validate our framework through a case study focused on a HRC disassembly desktop task. The findings confirm the system's capability to accurately predict both human intentions and trajectories. 

Abstract Human-robot collaboration (HRC) has become an integral element of many industries, including manufacturing. A fundamental requirement for safe HRC is to understand and predict human intentions and trajectories, especially when humans and robots operate in close proximity. However, predicting both human intention and trajectory components simultaneously remains a research gap. In this paper, we have developed a multi-task learning (MTL) framework designed for HRC, which processes motion data from both human and robot trajectories. The first task predicts human trajectories, focusing on reconstructing the motion sequences. The second task employs supervised learning, specifically a Support Vector Machine (SVM), to predict human intention based on the latent representation. In addition, an unsupervised learning method, Hidden Markov Model (HMM), is utilized for human intention prediction that offers a different approach to decoding the latent features. The proposed framework uses MTL to understand human behavior in complex manufacturing environments. The novelty of the work includes the use of a latent representation to capture temporal dynamics in human motion sequences and a comparative analysis of various encoder architectures. We validate our framework through a case study focused on a HRC disassembly desktop task. The findings confirm the system’s capability to accurately predict both human intentions and trajectories. 

Abstract This paper presents a deep learning enhanced adaptive unscented Kalman filter (UKF) for predicting human arm motion in the context of manufacturing. Unlike previous network-based methods that solely rely on captured human motion data, which is represented as bone vectors in this paper, we incorporate a human arm dynamic model into the motion prediction algorithm and use the UKF to iteratively forecast human arm motions. Specifically, a Lagrangian-mechanics-based physical model is employed to correlate arm motions with associated muscle forces. Then a Recurrent Neural Network (RNN) is integrated into the framework to predict future muscle forces, which are transferred back to future arm motions based on the dynamic model. Given the absence of measurement data for future human motions that can be input into the UKF to update the state, we integrate another RNN to directly predict human future motions and treat the prediction as surrogate measurement data fed into the UKF. A noteworthy aspect of this study involves the quantification of uncertainties associated with both the data-driven and physical models in one unified framework. These quantified uncertainties are used to dynamically adapt the measurement and process noises of the UKF over time. This adaption, driven by the uncertainties of the RNN models, addresses inaccuracies stemming from the data-driven model and mitigates discrepancies between the assumed and true physical models, ultimately enhancing the accuracy and robustness of our predictions. One unique point of our method is that it integrates a dynamic model of human arms and two RNN models, and uses Monte Carlo dropout sampling to quantify the uncertainties inherent in our RNN prediction models and transforms them into the covariances of the UKF’s measurement and process noises respectively. Compared to the traditional RNN-based prediction, our method demonstrates improved accuracy and robustness in extensive experimental validations of various types of human motions.