Learning Symbolic and Subsymbolic Temporal Task Constraints from Bimanual Human Demonstrations
<|reference_start|>Learning Symbolic and Subsymbolic Temporal Task Constraints from Bimanual Human Demonstrations: Learning task models of bimanual manipulation from human demonstration and their execution on a robot should take temporal constraints between actions into account. This includes constraints on (i) the symbolic level such as precedence relations or temporal overlap in the execution, and (ii) the subsymbolic level such as the duration of different actions, or their starting and end points in time. Such temporal constraints are crucial for temporal planning, reasoning, and the exact timing for the execution of bimanual actions on a bimanual robot. In our previous work, we addressed the learning of temporal task constraints on the symbolic level and demonstrated how a robot can leverage this knowledge to respond to failures during execution. In this work, we propose a novel model-driven approach for the combined learning of symbolic and subsymbolic temporal task constraints from multiple bimanual human demonstrations. Our main contributions are a subsymbolic foundation of a temporal task model that describes temporal nexuses of actions in the task based on distributions of temporal differences between semantic action keypoints, as well as a method based on fuzzy logic to derive symbolic temporal task constraints from this representation. This complements our previous work on learning comprehensive temporal task models by integrating symbolic and subsymbolic information based on a subsymbolic foundation, while still maintaining the symbolic expressiveness of our previous approach. We compare our proposed approach with our previous pure-symbolic approach and show that we can reproduce and even outperform it. Additionally, we show how the subsymbolic temporal task constraints can synchronize otherwise unimanual movement primitives for bimanual behavior on a humanoid robot.<|reference_end|>
@article{dreher2024learning,
    title={Learning Symbolic and Subsymbolic Temporal Task Constraints from
  Bimanual Human Demonstrations},
    author={Christian Dreher and Tamim Asfour},
    journal={arXiv preprint arXiv:2403.16953},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16953},
    primaryClass={cs.RO}
}