Yi Cheng

×10−2][2\times 10^{-3},2\times 10^{-2}] kg⋅\cdotm2 Initial base position (x,yx,y) [−0.5,0.5][-0.5,0.5] m Initial base orientation (yaw) [−π,π][-\pi,\pi] rad Initial base linear velocity [−0.5,0.5][-0.5,0.5] m/s Initial base angular velocity [−0.5,0.5][-0.5,0.5] rad/s Initial joint position scale [0.5,1.5][0.5,1.5] – Depth image bias [−0.04,0.04][-0.04,0.04] m Depth image noise (σ\sigma) 0.020.02 m Depth hole probability 0.030.03 – IV EXPERIMENT TABLE V: ABLATION STUDY RESULTS OF THE PRIOR FRAMEWORK ON TERRAIN ADAPTABILITY Method Mean Level Pyramid Stairs Inverted Stairs Boxes Plane Mean Reward PRIOR (ours) 5.7533 1.0 1.0000 1.0000 1.0 26.3462 PRIOR w/o reference gait 5.7735 1.0 1.0000 1.0000 1.0 23.7233 PRIOR w/o 𝐦^t\hat{\mathbf{m}}_{t} 5.7672 1.0 0.7734 1.0000 1.0 13.1775 PRIOR w/o 𝐝tH2\mathbf{d}_{t}^{\mathrm{H2}} 5.4627 1.0 0.3750 0.9687 1.0 10.1463 PRIOR with H1 = 6 5.7417 1.0 1.0000 1.0000 1.0 19.3234 PRIOR w/o landing state reward 5.7403 1.0 1.0000 1.0000 1.0 22.6262 IV-A Experimental Setting Based on the optimization architecture described in Section III-C, we conduct high-throughput policy training on a single NVIDIA RTX 4090 (