6 papers
Hybrid Feedback Sampling for Sample-Efficient Model Predictive Control
Chaoyi Pan, Zeji Yi, John Zhang +3
Thanks to its parallelizability and flexibility, sampling-based Model Predictive Control (MPC) has become widely popular for controlling real-world robotic systems. However, for hi…
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
John Z. Zhang, Maks Sorokin, Jan Brüdigam +14
This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by perfor…
Whole-Body Model-Predictive Control of Legged Robots with MuJoCo
John Z. Zhang, Taylor A. Howell, Zeji Yi +6
We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (i…
The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
Kevin Tracy, John Z. Zhang, Jon Arrizabalaga +4
We present a unified framework for solving trajectory optimization problems in a derivative-free manner through the use of sequential convex programming. Traditionally, nonconvex o…
Multi-IMU Sensor Fusion for Legged Robots
Shuo Yang, Zixin Zhang, John Z. Zhang +2
This paper presents a state-estimation solution for legged robots that uses a set of low-cost, compact, and lightweight sensors to achieve low-drift pose and velocity estimation un…
Wallbounce : Push wall to navigate with Contact-Implicit MPC
Xiaohan Liu, Cunxi Dai, John Z. Zhang +3
In this work, we introduce a framework that enables highly maneuverable locomotion using non-periodic contacts. This task is challenging for traditional optimization and planning m…