# Multi-Robot Task Allocation

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arXiv Nov 10, 2026
Discrete diffusion drops offline training: DMBD optimizes discrete variables directly and beats sampling baselines in 14 of 15 multi-robot task-allocation settings
Discrete diffusion for combinatorial optimization has relied on an offline-trained neural denoiser, while training-free model-based diffusion works only on continuous variables and must round its solutions. DMBD replaces the denoising step with Monte Carlo sampling weighted by the known objective to approximate categorical reverse transitions directly; on a heterogeneous multi-robot coalition-formation and scheduling benchmark it reaches the lowest makespan among sampling-based methods in 14 of 15 settings and beats all baselines in 8.
Discrete diffusion for combinatorial optimization has relied on an offline-trained neural denoiser, while training-free model-based diffusion works only on continuous variables and must round its solutions. DMBD replaces the denoising step with Monte Carlo sampling weighted by the known objective to approximate categorical reverse transitions directly; on a heterogeneous multi-robot coalition-formation and scheduling benchmark it reaches the lowest makespan among sampling-based methods in 14 of 15 settings and beats all baselines in 8.
Haejoon Lee（First author）
Discrete Model-Based Diffusion Combinatorial Optimization Multi-Robot Task Allocation Sampling-Based Optimization Discrete Diffusion
Scientific Reports Oct 20, 2026
Hospital delivery robots stop competing for the same job: hierarchical policy cuts weighted tardiness from 2359 to 29 and lifts STAT on-time rate to 0.969
Hospital delivery robots must decide both which request to take and in what order to visit stops, while STAT and routine items carry different clinical deadlines. HMART puts that joint decision into a shared graph-attention encoder with two sequential decoders, bounds the tail of weighted tardiness with a conditional value-at-risk constraint, and coordinates the shared elevators through a model predictive control layer. In a discrete-event simulation of a four-floor hospital with three elevators and twelve robots, the engineered-decoder instantiation lowers mean total weighted tardiness from 2359±214 to 29±6 against the strongest dispatching rule and raises the STAT on-time rate from 0.925±0.011 to 0.969±0.007.
Hospital delivery robots must decide both which request to take and in what order to visit stops, while STAT and routine items carry different clinical deadlines. HMART puts that joint decision into a shared graph-attention encoder with two sequential decoders, bounds the tail of weighted tardiness with a conditional value-at-risk constraint, and coordinates the shared elevators through a model predictive control layer. In a discrete-event simulation of a four-floor hospital with three elevators and twelve robots, the engineered-decoder instantiation lowers mean total weighted tardiness from 2359±214 to 29±6 against the strongest dispatching rule and raises the STAT on-time rate from 0.925±0.011 to 0.969±0.007.
Khulna University of Engineering and Technology
Kazi Md. Tanvir Anzum（First author, Corresponding author）
HMART Hierarchical Multi-Agent Reinforcement Learning Model Predictive Control Conditional Value-at-Risk Multi-Robot Task Allocation
researchsquare.com Oct 23, 2026
Welding robots stop splitting dissimilar-steel work by distance alone: thermal, stress and carbon coupling enter the objective, cutting simulated makespan and energy
Task allocation for collaborative robotic welding of dissimilar steel joints no longer divides work by robot-to-seam distance alone. The framework treats each robot as a player in an α-potential game and uses a physics-informed neural-network surrogate for repeated thermo-mechanical evaluation, so allocation accounts for residual stress and carbon-depleted-zone width. Across computational cases with 50 tasks and 2 robots, 100 tasks and 3 robots, and 200 tasks and 4 robots, it reduced normalized makespan and energy relative to distance-driven allocation while keeping lower predicted residual stress and carbon-depleted-zone width; these are computational predictions requiring independent experimental validation.
Task allocation for collaborative robotic welding of dissimilar steel joints no longer divides work by robot-to-seam distance alone. The framework treats each robot as a player in an α-potential game and uses a physics-informed neural-network surrogate for repeated thermo-mechanical evaluation, so allocation accounts for residual stress and carbon-depleted-zone width. Across computational cases with 50 tasks and 2 robots, 100 tasks and 3 robots, and 200 tasks and 4 robots, it reduced normalized makespan and energy relative to distance-driven allocation while keeping lower predicted residual stress and carbon-depleted-zone width; these are computational predictions requiring independent experimental validation.
University of Bonab
Mohammad Reza Omidi（First author, Corresponding author）
α-Potential Game Physics-Informed Neural Network Robotic Welding Multi-Robot Task Allocation Log-Linear Learning

You’re all caught up

Discrete diffusion drops offline training: DMBD optimizes discrete variables directly and beats sampling baselines in 14 of 15 multi-robot task-allocation settings

Hospital delivery robots stop competing for the same job: hierarchical policy cuts weighted tardiness from 2359 to 29 and lifts STAT on-time rate to 0.969

Welding robots stop splitting dissimilar-steel work by distance alone: thermal, stress and carbon coupling enter the objective, cutting simulated makespan and energy