# LLA-MPPI：利用 GPU 加速并行仿真实现腿式机器人快速自适应全身控制

> 原文链接：https://robotics.hellobike.com/papers/2026-10-07-2610-10465
> 频道：papers | 更新日期：2026-10-07 | 标签：arxiv, cs.RO
> 核心摘要：LLA-MPPI通过选择GPU批量接触仿真器实现腿式机器人全身自适应控制，并在仿真任务和Unitree Go2上验证。

# LLA-MPPI：利用 GPU 加速并行仿真实现腿式机器人快速自适应全身控制

::: v-pre
> 原文链接：[LLA-MPPI: Rapidly Adaptive Whole-body Control of Legged Robots with GPU-Accelerated Parallel Simulations](https://arxiv.org/abs/2610.10465)（arXiv 2610.10465 · 发布日期：2026-10-07）

**LLA-MPPI: Rapidly Adaptive Whole-body Control of Legged Robots with GPU-Accelerated Parallel Simulations**

LLA-MPPI通过选择GPU批量接触仿真器实现腿式机器人全身自适应控制，并在仿真任务和Unitree Go2上验证。

## 核心要点

- 固定标称模型的实时全身控制在部署动力学变化时容易退化。
- 方法用窗口预测误差在GPU批量接触仿真器库中选择最能解释近期运动的模型。
- 选中模型供全身MPPI规划控制，无需离线训练，且所选假设具有物理可解释性。
- 四个仿真任务成功率为97.5%，硬件展示了负载变化、单腿失效和推箱增重场景；代码、视频和项目详情已公开。

**验证范围与局限**：验证范围为四个仿真任务和一台Unitree Go2上的硬件演示。

## 英文摘要 (Abstract)

Real-time whole-body controllers for legged robots typically plan through a fixed nominal model and degrade when the deployed dynamics change. Adaptive methods typically require a model structure that contact dynamics do not provide, or they need offline training for each anticipated condition. We present Look-back and Look-ahead Adaptive Model Predictive Path Integral control (LLA-MPPI). The method converts whole-body adaptation into selection over a bank of GPU-batched contact simulators with different physical or structural parameters. Windowed prediction errors select the simulator that best explains recent motion. A whole-body MPPI planner optimizes controls through the selected model. The framework requires no offline training, and its selected hypotheses are physically interpretable. Across four simulated tasks, it achieves 97.5% success while the strongest baseline reaches 74% and an oracle with the true model reaches 98.5%. Hardware validation on a Unitree Go2 shows the robot walking under a payload added mid-run, walking after one leg is disabled, and pushing a box to its goal while increasing its mass on the fly. Code, videos, and project details are available at: https://lla-control.github.io

作者：Sebin Jung, Maitham F. AL-Sunni, Juan Alvarez-Padilla, Zachary R. Manchester, Changliu Liu, John M. Dolan
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## 原始参考来源 (Sources)

- [arXiv 2610.10465：LLA-MPPI: Rapidly Adaptive Whole-body Control of Legged Robots with GPU-Accelerated Parallel Simulations](https://arxiv.org/abs/2610.10465)