让机器人在复杂地形上稳定走1.5公里，靠的是精准感知和长期决策优化。
SOLO: Stable Omni-terrain Long-Horizon Perceptive Humanoid Locomotion
- 用傅里叶编码查询重建地形细节，保留尖锐边界
- 引入未来状态差异惩罚，让早期动作学会承担后果
- 仅靠胸前摄像头+本体感觉，零样本部署成功穿越户外长距离路线
人类能长时间穿越复杂地形而不失衡，而感知型人形机器人在长时程中会因感知与控制误差累积变得脆弱。本文提出SOLO，一种统一框架，解决两大导致长时程脆弱性的根源：密集地形重建会模糊关键行动细节，逐点模仿缺乏时间信用分配。其查询重构器（QR）利用傅里叶编码的单元查询，从深度-本体感知标记中检索空间特异性证据，保持地形边界清晰。轨迹感知均方误差（TA-MSE）蒸馏将下一状态教师-学生差异加入PPO奖励，使广义优势估计可将未来差异惩罚回传至前期动作。仿真中，QR使高程图L1误差降低3.3-4.0倍；TA-MSE在课程推进中超越PPO与MSE+PPO。在压力测试地形上，SOLO实现97.5%平均通行成功率和96%踏石成功率，而密集重构变体仅为75.0-75.6%和0-3%。仅使用胸挂式深度相机与本体感觉，零样本部署下完成1.5公里户外连续路线及室内混合地形任务。项目页面：https://sunpihai-up.github.io/solo/
原文摘要 · Abstract (English)
Humans traverse complex terrain over long distances without losing balance, whereas perceptive humanoid policies become fragile as perception and control errors accumulate. We present SOLO, a unified framework addressing two compounding causes of this long-horizon fragility: dense terrain reconstruction smooths action-critical details, and pointwise imitation lacks temporal credit assignment. Its Query Reconstructor (QR) uses Fourier-encoded cell queries to retrieve spatially specific evidence from depth-proprioception tokens, preserving sharp terrain boundaries. Trajectory-Aware MSE (TA-MSE) Distillation adds next-state teacher-student disagreement to the PPO reward, enabling Generalized Advantage Estimation to propagate future disagreement penalties to preceding actions. In simulation, QR reduces height-map L1 error by factors of 3.3-4.0, while TA-MSE surpasses PPO and MSE+PPO in curriculum progression. On stress-test terrains, SOLO achieves 97.5% mean traversal success and 96% stepping-stone success, versus 75.0-75.6% and 0-3% for dense-reconstructor variants. Deployed zero-shot with only a chest-mounted depth camera and proprioception, SOLO completes a continuous 1.5-km outdoor route and an indoor mixed-terrain course. Project page: https://sunpihai-up.github.io/solo/
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