发表机构
Carnegie Mellon University; Massachusetts Institute of Technology（卡内基梅隆大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出LLA-MPPI方法，通过GPU并行模拟器选择实现腿式机器人全身自适应控制，无需离线训练，在模拟和硬件上均显著优于基线。
AI 中文摘要
腿式机器人的实时全身控制器通常通过固定的标称模型进行规划，并在部署动力学发生变化时性能下降。自适应方法通常需要接触动力学无法提供的模型结构，或者需要针对每种预期条件进行离线训练。我们提出了回望与前瞻自适应模型预测路径积分控制（LLA-MPPI）。该方法将全身自适应转化为对一组具有不同物理或结构参数的GPU批处理接触模拟器的选择。窗口化预测误差选择最能解释近期运动的模拟器。全身MPPI规划器通过所选模型优化控制。该框架无需离线训练，其选择的假设具有物理可解释性。在四个模拟任务中，它达到了97.5%的成功率，而最强的基线达到74%，具有真实模型的oracle达到98.5%。在Unitree Go2上的硬件验证显示，机器人在运行中增加负载时行走，在一条腿被禁用后行走，以及在推动箱子到达目标的同时动态增加其质量。代码、视频和项目详情可在以下网址获取：此https URL。
英文摘要
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
CommentsThe first two authors are co-first authors (contributed equally to the work)
