Agentic Self-Evolutionary Replanning for Embodied Navigation

Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficient online updates but remain constrained by static loss functions and gradient-only parameter updates. We propose Self-Evolutionary RePlanning (SERP), a failure-aware framework that jointly adapts motion-planner parameters and loss function weights from closed-loop execution feedback. SERP provides two adaptation mechanisms. First, in-context learning with auto-differentiation (ILAD) enables rapid online adaptation by coupling local numerical refinement with history-conditioned semantic intervention. Auto-differentiation performs numerical updates, while a vision-language model (VLM) interprets recent adaptation history to jointly revise planner parameters and loss weights when local adaptation becomes ineffective. Second, the memory-augmented rollout system (MARS) extends ILAD into a deliberative and evolutionary recovery process with memory-guided candidate generation and parallel low-fidelity rollout evaluation. The resulting recovery trace is then consolidated into recovery memory to guide future adaptation. Extensive simulation and real-world experiments demonstrate improved failure recovery performance over various baselines across diverse environments.