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Abstract Human–robot collaboration (HRC) has become an integral element of many manufacturing and service industries. A fundamental requirement for safe HRC is understanding and predicting human trajectories and intentions, especially when humans and robots operate nearby. Although existing research emphasizes predicting human motions or intentions, a key challenge is predicting both human trajectories and intentions simultaneously. This paper addresses this gap by developing a multi-task learning framework consisting of a bi-long short-term memory-based encoder–decoder architecture that obtains the motion data from both human and robot trajectories as inputs and performs two main tasks simultaneously: human trajectory prediction and human intention prediction. The first task predicts human trajectories by reconstructing the motion sequences, while the second task tests two main approaches for intention prediction: supervised learning, specifically a support vector machine, to predict human intention based on the latent representation, and, an unsupervised learning method, the hidden Markov model, that decodes the latent features for human intention prediction. Four encoder designs are evaluated for feature extraction, including interaction-attention, interaction-pooling, interaction-seq2seq, and seq2seq. The framework is validated through a case study of a desktop disassembly task with robots operating at different speeds. The results include evaluating different encoder designs, analyzing the impact of incorporating robot motion into the encoder, and detailed visualizations. The findings show that the proposed framework can accurately predict human trajectories and intentions.
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A Trust-Assist Framework for Human–Robot Co-Carry Tasks
Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework.
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- PAR ID:
- 10423874
- Date Published:
- Journal Name:
- Robotics
- Volume:
- 12
- Issue:
- 2
- ISSN:
- 2218-6581
- Page Range / eLocation ID:
- 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/robotics12020030
