Predictive Training with Latent Imagination for Visual Quadruped Navigation
Reinforcement-learning navigation policies for legged robots select actions reactively from current observations and short-term memory, with limited capacity to anticipate how moving obstacles will evolve in the near future. In dynamic environments, this reactivity causes the robot to respond too late because collision risk depends on short-horizon scene structure rather than on current obstacle positions alone. Lightweight predictive supervision applied to the policy's recurrent state during training can encode anticipatory obstacle dynamics without modifying the inference-time controller. We augment a reactive LSTM-SRU navigation backbone with an auxiliary JEPA-style predictor and SIGRe...
| Authors | Yancheng Zhu, Wanli Ma, Chen Han et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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