Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies

@inproceedings{Rodriguez2022NeuralGL,
  title={Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies},
  author={Ivan Dario Jimenez Rodriguez and Noel Csomay-Shanklin and Yisong Yue and A. Ames},
  booktitle={L4DC},
  year={2022}
}
This work presents Neural Gaits, a method for learning dynamic walking gaits through the enforce-ment of set invariance that can be refined episodically using experimental data from the robot. We frame walking as a set invariance problem enforceable via control barrier functions (CBFs) defined on the reduced-order dynamics quantifying the underactuated component of the robot: the zero dynamics. Our approach contains two learning modules: one for learning a policy that satisfies the CBF condition… 
View With Semantic Reader

Figures and Tables from this paper

References

SHOWING 1-10 OF 31 REFERENCES
Learning quadrupedal locomotion over challenging terrain
TLDR
The presented work indicates that robust locomotion in natural environments can be achieved by training in simple domains.
Robust Feedback Motion Policy Design Using Reinforcement Learning on a 3D Digit Bipedal Robot
In this paper, a hierarchical and robust framework for learning bipedal locomotion is presented and successfully implemented on the 3D biped robot Digit built by Agility Robotics. We propose a…
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
TLDR
The notion of projection-to-state safety paired with a machine learning framework is utilized in an attempt to learn the model uncertainty as it affects the barrier functions in the setting of bipedal locomotion.
Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition
TLDR
A reward-specification framework based on composing simple probabilistic periodic costs on basic forces and velocities is proposed and instantiate this framework to define a parametric reward function with intuitive settings for all common bipedal gaits - standing, walking, hopping, running, and skipping.
Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control
TLDR
This paper presents an implementation of model predictive control (MPC) to determine ground reaction forces for a torque-controlled quadruped robot, capable of robust locomotion at a variety of speeds.
Control barrier function based quadratic programs with application to bipedal robotic walking
TLDR
The end result is the generation of stable walking satisfying physical realizability constraints for a model of the bipedal robot AMBER2.
First steps toward formal controller synthesis for bipedal robots with experimental implementation
MPC for Humanoid Gait Generation: Stability and Feasibility
TLDR
An intrinsically stable Model Predictive Control framework for humanoid gait generation that incorporates a stability constraint in the formulation is presented and it is proved that recursive feasibility guarantees stability of the CoM/ZMP dynamics.
Fast Online Trajectory Optimization for the Bipedal Robot Cassie
TLDR
Preliminary results on the physical robot show functionality of the operational space control system, with integration of the trajectory planner a work in progress, and simulation results showing the performance and robustness to disturbances of the planning and control framework are presented.
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot
TLDR
This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments and presents a state estimator formulation that permits highly precise execution of extended walking plans over non-flat terrain.
...
...