无需预设步态，机器人自动生成复杂运动行为。
Reference-Free Sampling-Based Model Predictive Control
- 用位置和速度控制点的三次样条参数化优化运动轨迹
- 仅需少量采样即可实现实时控制，支持跳跃、倒立等动作
- 适合需要自适应运动策略的四足与人形机器人研究
我们提出一种基于采样的模型预测控制（MPC）框架，可在不依赖人工设计步态或预定义接触序列的情况下实现涌现式运动。该方法通过优化高层目标，自主发现从慢跑到疾跑、稳健站立、跳跃以及手倒立平衡等多种运动模式。基于模型预测路径积分（MPPI），我们引入一种在位置和速度控制点上操作的三次赫米特样条参数化方法，使接触生成与断开策略能自动适应任务需求，仅需少量采样轨迹即可实现高效控制。该方法具备极高的样本效率，可在标准CPU上实现实时运行，无需通常所需的GPU加速。我们在Go2四足机器人上验证了该方法，展示了多种涌现步态和基础跳跃能力。在仿真中，进一步展现了后空翻、动态手倒立平衡及人形机器人行走等复杂行为，且无需参考轨迹追踪或离线预训练。
原文摘要 · Abstract (English)
We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences. Our method discovers diverse motion patterns, ranging from trotting to galloping, robust standing policies, jumping, and handstand balancing, purely through the optimization of high-level objectives. Building on model predictive path integral (MPPI), we propose a cubic Hermite spline parameterization that operates on position and velocity control points. Our approach enables contact-making and contact-breaking strategies that adapt automatically to task requirements, requiring only a limited number of sampled trajectories. This sample efficiency enables real-time control on standard CPU hardware, eliminating the GPU acceleration typically required by other state-of-the-art MPPI methods. We validate our approach on the Go2 quadrupedal robot, demonstrating a range of emergent gaits and basic jumping capabilities. In simulation, we further showcase more complex behaviors, such as backflips, dynamic handstand balancing and locomotion on a Humanoid, all without requiring reference tracking or offline pre-training.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。