Optimiser Repairs for Living-Cell Self-Driving Laboratories: Performance and Redundancy under Experimental Constraints

Living-cell self-driving laboratories must optimise experiments despite delayed readouts, changing responses and limited sample availability. In simulations, we evaluate when optimiser repairs improve closed-loop performance and when platform constraints make them redundant, separating consumption of a sample from permanent loss of the ability to repeat its condition. We prove that exclusions and confirmation requests produce exactly the baseline trajectory when they act only on conditions already forbidden by the platform, share the same posterior and selection procedure, and release infeasible requested slots. This result covers the evaluated pending-exclusion, previous-batch and confirmation rules under permanent condition retirement; it does not cover arbitrary selection interventions. We then conduct 33,900 simulated policy runs with 200-300 paired campaign seeds per setting. A public PBMC reference supports characterisation of the target-label readout, while condition-response landscapes remain synthetic. Across 27 landscape, assay-size and kernel settings, previous-batch exclusion changes final normalised performance by -0.004 to +0.283, with positive differences surviving Holm adjustment in 13 settings. Effects are small on the rugged landscape. Permanent retirement can be harmful under noisy rugged responses. All three subsumed interventions reproduce every saved baseline event on 200 of 200 paired seeds. These results establish the importance of explicit availability constraints and evaluation across experimental regimes when assessing optimiser repairs.