Reconstruction and Discrete-Event Simulation Assessment of an Analytical

This study compares two implemented intra-aisle robotic-picking configurations through discrete-event simulation. Without an onboard tote lift, two level-captive shuttles serve the two tested levels; with the lift, one shuttle remains on its horizontal level and its onboard mechanism moves the order tote to allow picks from the adjacent upper level. Thirty configuration-task-size experiments (n = 1-15 locations; ten replications each) yield 300 replication-level cycle-time observations. Two-sided Welch tests are applied to the replication means, with Holm adjustment across the 15 comparisons. The onboard-tote-lift configuration has longer mean cycle time at all tested task sizes. At n = 15, the means are 299.477 and 333.231 s/cycle, respectively: a 33.753 s increase (11.27%). The corresponding cycle-time-derived item rates are 180.314 and 162.050 items/h (-10.13%). These rates are calculated as 3600n divided by the scenario-mean cycle time; they are not measurements of the combined concurrent capacity of two level-captive shuttles. Importantly, task locations are sampled from different eligible-level sets in the two configurations, so the result concerns the implemented configuration-and-task-generation packages, not the isolated effect of a five-second tote-lift operation under identical picked locations. The study quantifies a cycle-performance difference alongside a reduction from two dedicated shuttles to one within the tested two-level block; it does not quantify system-level maximum throughput, investment cost, or reliability.

We present R2HandoverSim, a simulation benchmark for robot-to-human (R2H) object handovers. Although R2H handover methods have advanced rapidly, the lack of standardized evaluation protocols impedes objective comparison. Our benchmark enables reproducible evaluation by systematically comparing four baselines on their predicted shared grasp poses. We conduct a user study with 30 participants, analyze baseline performance, and show that simulation results correlate with real-world evaluation outcomes. Crucially, five complementary metrics (planning feasibility, reachability, grasp stability, grasp affordance, and safety) better reflect user-perceived handover quality than overall success rate alone. Website and code: https://robot-future.github.io/r2handoversim/.

Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) models the multi-robot shelf rearrangement problem in automated warehouses. MAPF-DECOMP is a recent framework that first computes collision-free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it enforces strict trajectory dependencies, often leading to poor execution quality due to idle agents and unnecessary shelf switching. We introduce CREST, a new execution framework that achieves more continuous shelf carrying by proactively releasing trajectory constraints during execution. Experiments on diverse warehouse layouts show that CREST consistently outperforms MAPF-DECOMP, reducing metrics related to agent travel, makespan, and shelf switching by up to 40.5%, 33.3%, and 44.4%, respectively, with even greater benefits under lift/place overhead. These results underscore the importance of execution-aware constraint release for scalable warehouse rearrangement. Code and data are available at https://github.com/ChristinaTan0704/CREST.