DiG-Nav: Learning Differentiable Goal Allocation for Multi-Object Navigation

Multi-object navigation requires an agent to prioritize unvisited targets while learning continuous actions under partial observability. We introduce DiG-Nav, which encodes the remaining goal set with masked self-attention and computes a differentiable allocation over goals. The weighted goal representation conditions the action policy, while a short-horizon latent rollout supplies an auxiliary value objective for learning target priorities. Alternating model fitting and policy improvement preserves gradients through imagined actions without optimizing the predictor toward inflated values. We characterize the sensitivity of allocation probabilities and distinguish temperature smoothing from changes in the highest-ranked goal. On Gibson, Matterport3D, and Flightmare, DiG-Nav achieves episode success rates of 83.6%, 67.1%, and 69.5%, respectively, exceeding the strongest compared baseline by 3.1, 5.3, and 4.2 percentage points. Component ablations support the contributions of learned allocation and predictive supervision. Real-world quadrotor demonstrations illustrate continuous execution across multiple targets.