  As human-robot collaboration increases in the workforce, it becomes essential
for human-robot teams to coordinate efficiently and intuitively. Traditional
approaches for human-robot scheduling either utilize exact methods that are
intractable for large-scale problems and struggle to account for stochastic,
time varying human task performance, or application-specific heuristics that
require expert domain knowledge to develop. We propose a deep learning-based
framework, called HybridNet, combining a heterogeneous graph-based encoder with
a recurrent schedule propagator for scheduling stochastic human-robot teams
under upper- and lower-bound temporal constraints. The HybridNet's encoder
leverages Heterogeneous Graph Attention Networks to model the initial
environment and team dynamics while accounting for the constraints. By
formulating task scheduling as a sequential decision-making process, the
HybridNet's recurrent neural schedule propagator leverages Long Short-Term
Memory (LSTM) models to propagate forward consequences of actions to carry out
fast schedule generation, removing the need to interact with the environment
between every task-agent pair selection. The resulting scheduling policy
network provides a computationally lightweight yet highly expressive model that
is end-to-end trainable via Reinforcement Learning algorithms. We develop a
virtual task scheduling environment for mixed human-robot teams in a
multi-round setting, capable of modeling the stochastic learning behaviors of
human workers. Experimental results showed that HybridNet outperformed other
human-robot scheduling solutions across problem sizes for both deterministic
and stochastic human performance, with faster runtime compared to
pure-GNN-based schedulers.
