Reference-Free Sampling-Based Model Predictive Control
This work addresses the challenge of adaptive and efficient robot motion control for robotics applications, representing a novel method for a known bottleneck rather than a foundational breakthrough.
The authors tackled the problem of enabling emergent locomotion in robots without predefined gait patterns by developing a sampling-based model predictive control framework, resulting in diverse motion patterns like trotting, galloping, and handstand balancing, with real-time control on standard CPU hardware and elimination of GPU acceleration typically required by other methods.
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.