Predictive Training with Latent Imagination for Visual Quadruped Navigation
This work addresses the challenge of reactive navigation in quadrupedal robots operating in dynamic environments, where reliance solely on current observations often leads to delayed obstacle avoidance and collisions due to an inability to anticipate moving obstacles. To overcome this limitation, the authors propose a foresighted navigation method that incurs zero inference overhead by incorporating a lightweight predictive supervision signal during training. Leveraging a JEPA-style auxiliary predictor and SIGReg regularization, the approach guides the policy network’s hidden states to implicitly encode future scene dynamics without altering the inference-time controller architecture. Built upon an LSTM-SRU backbone within an end-to-end reinforcement learning framework, the method significantly improves navigation success rates and reduces collision frequency in both simulated and real-world dynamic environments, achieving zero-shot sim-to-real transfer on the Unitree Go2 platform without fine-tuning.