Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control
By Donghyun Kim, Jared Di Carlo, Benjamin Katz, Gerardo Bledt, Sangbae Kim. arXiv.org, 2019
445 citations recorded by the source corpus.
Dynamic legged locomotion is a challenging topic because of the lack of established control schemes which can handle aerial phases, short stance times, and high-speed leg swings. In this paper, we propose a controller combining whole-body control (WBC) and model predictive control (MPC). In our framework, MPC finds an optimal reaction force profile over a longer time horizon with a simple model, and WBC computes joint torque, position, and velocity commands based on the reaction forces computed from MPC. Unlike existing WBCs, which attempt to track commanded body trajectories, our controller is focused more on the reaction force command, which allows it to accomplish high speed dynamic locomotion with aerial phases. The newly devised WBC is integrated with MPC and tested on the Mini-Cheetah quadruped robot. To demonstrate the robustness and versatility, the controller is tested on six different gaits in a number of different environments, including outdoors and on a treadmill, reaching a top speed of 3.7 m/s.
Subfields: Biomedical Engineering, Genetics
View Paper: https://www.semanticscholar.org/paper/90e8e65532a647074fa705e8c84d70fa0e1af266
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Gabriel B. Margolis
- N. Rudin
- David Hoeller
- Dhionis V. Sako
- Pulkit Agrawal
- Tao Chen
- Marco Hutter
- Jitendra Malik
In Genetics
Citations in the corpus
- Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior (2022, methodology): “To overcome these issues, we leverage human intuition about useful behavior parameters (bt) corresponding to gait properties like foot swing motion, body posture, and contact schedule [6, 7, 8, 23].”
- Rapid locomotion via reinforcement learning (2022, methodology): “We also deployed the model-predictive controller from [21] in scenarios (1) and (4), which were the most convenient to replicate.”
- ANYmal parkour: Learning agile navigation for quadrupedal robots (2023, background): “More recently, the research continued with quadrupedal robots running [11] and jumping over obstacles [12], [13].”