Incremental Adaptation of Human-Robot Collaborative Missions using Hybrid Task Planning

As robots increasingly collaborate with humans on complex missions, coordinating tasks under functional and non-functional constraints becomes more challenging, particularly in dynamically changing environments. Uncertainty arising from unpredictable human behaviour, environmental variability, and system failures further complicates the synthesis of robust task plans that ensure mission success while optimising cost, safety, and performance. Further, the presence of multiple, often conflicting objectives, such as maximising the probability of mission success while minimising expected execution cost, together with probabilistic action outcomes, exacerbates the combinatorial complexity of providing formal guarantees. Addressing these challenges entails that effective planning approaches must combine scalability with rigorous verification and provide adaptive capabilities to cope with runtime disruptions. We introduce ARCH (Adaptive Robot–Human Collaboration using Hybrid Planning), an approach for synthesising verified and adaptable task plans for complex Cyber-Physical-Human Systems (CPHS). ARCH integrates off-the-shelf numerical planning, probabilistic model checking, and multi-objective evolutionary algorithms to generate Pareto-optimal sets of task plans that satisfy probabilistic requirements. To support efficient adaptation, ARCH reuses previously synthesised plans that remain feasible after changes, seeding the evolutionary search and guiding it towards promising regions of the solution space. We evaluate ARCH using two industrial CPHS case studies and conduct an empirical analysis of its effectiveness and scalability. The results demonstrate that seeding can significantly improve search efficiency during adaptation and that ARCH scales to problem instances where traditional probabilistic planning approaches become impractical due to state explosion.