Nav4Manip: From Navigation to Dexterous Mobile Manipulation through Co-Evolution

Dexterous mobile manipulation is essential for robots to perform useful tasks in complex, unstructured environments. However, existing systems face two challenges. First, navigation remains too coarse for downstream manipulation, which often requires a precise base position and approach orientation rather than merely reaching a meter-scale goal region. Second, navigation and manipulation are commonly trained in isolation, lacking an interactive feedback loop that enables the two capabilities to co-evolve. These limitations prevent current systems from reliably solving practical, long-horizon dexterous mobile-manipulation tasks. To address them, we introduce Nav4Manip, a bidirectionally coupled co-evolution system for long-horizon dexterous mobile manipulation. It comprises two complementary architectures: Nav4Manip-N learns long-horizon navigation and manipulation-aware stopping, while Nav4Manip-M post-trains a world-action model (WAM) to provide the downstream dexterous-manipulation interface. We train Nav4Manip-N in three stages: multi-task co-tuning learns navigation behavior and manipulation-aware affordance prediction; rejection sampling fine-tuning (RFT) rejects low-quality policy-generated paths and fine-tunes the model on retained manipulation-successful trajectories; and reinforcement learning from AI feedback (RLAIF) uses execution-grounded counterfactual preferences to further improve navigation decisions. To support this framework, we establish the Nav-N-Manip benchmark and construct Nav4Manip-Pairs from benchmark rollouts, providing large-scale paired navigation–manipulation trajectories and rollout-derived affordance supervision. Extensive experiments on established vision-language navigation (VLN) benchmarks and Nav-N-Manip demonstrate state-of-the-art performance, while real-world evaluations show that Nav4Manip completes diverse dexterous mobile-manipulation tasks. Project website: https://nav4manip-anonymous.github.io/Nav4Manip.