论文/arXiv 2608.22033·2 个月前

DELTA：用于稀疏地形四足运动的可变形高程局部地形注意力编码器

DELTA用可变形局部高程注意力编码器降低稀疏地形四足落足感知成本，并在仿真与RAIBO2真机验证。

AI 整理

• 本体 SDK 与控制
• 仿真与训练
• 具身模型

原文链接：DELTA: Deformable Elevation-Based Local Terrain Attention Encoder for Sparse-Terrain Quadrupedal Locomotion（arXiv 2608.22033 · 发布日期：2026-08-22）

DELTA: Deformable Elevation-Based Local Terrain Attention Encoder for Sparse-Terrain Quadrupedal Locomotion

DELTA用可变形局部高程注意力编码器降低稀疏地形四足落足感知成本，并在仿真与RAIBO2真机验证。

核心要点

• 稀疏地形稳定行走需选择与状态相关的地形证据，以实现精确落足。
• DELTA按状态预测采样位置，从自适应局部高程块形成固定数量证据token。
• 在标准分辨率下最终通过性能与AME相当，同时提升学习效率。
• 固定编码成本支持更高分辨率地图，并泛化到未见连续/离散混合路线。

验证范围与局限：验证范围包括仿真中的标准与高分辨率稀疏地形、未见混合评估路线，以及RAIBO2真机sim-to-real迁移。

英文摘要 (Abstract)

Stable quadrupedal locomotion on sparse terrain requires selecting state-relevant terrain evidence for precise foot placement. Model-based foothold planners provide precise foothold selection but rely heavily on explicit model assumptions. Recent attention-based map encoding (AME) studies show that end-to-end reinforcement learning (RL) can learn implicit foothold guidance. However, the computational cost of dense AME encoding grows with map resolution, limiting its scalability to fine-grained sparse terrain. We propose DELTA, a Deformable Elevation-Based Local Terrain Attention encoder. DELTA predicts state-conditioned sampling locations, forms terrain evidence tokens from adaptive local elevation patches, and attends only to a fixed-size token set. With fixed sampling and patch settings, DELTA's encoder cost is independent of map resolution. Experiments show that DELTA achieves final traversal performance comparable to AME at the standard resolution while improving learning efficiency. This fixed encoder cost enables the use of higher-resolution terrain maps, improving traversal on fine-grained sparse terrain. DELTA also demonstrates strong generalization to unseen mixed evaluation courses composed of continuous and discrete terrain elements. Beyond simulation, DELTA demonstrates successful sim-to-real transfer on RAIBO2. Analysis of the learned sampling offsets and attention weights shows that DELTA samples steppable regions and attends to terrain evidence relevant to future touchdowns without foothold labels or attention supervision.

作者：Sanghyun Park, Moonkyu Jung, Jemin Hwangbo

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2608.22033：DELTA: Deformable Elevation-Based Local Terrain Attention Encoder for Sparse-Terrain Quadrupedal Locomotion访问 2026-10-21