Trajectory Optimization with Optimization-Based Dynamics

We present a framework for bi-level trajectory optimization in which a system's dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics represe...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 7; no. 3; pp. 6750 - 6757
Main Authors: Howell, Taylor A., Le Cleac'h, Simon, Singh, Sumeet, Florence, Pete, Manchester, Zachary, Sindhwani, Vikas
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerodynamics
Complex systems
Constraints
Coulomb friction
Dynamics
Friction
Heuristic algorithms
Iterative methods
Locomotion
motion and path planning
Optimization
optimization and optimal control
Planning
Representations
Task analysis
Trajectory optimization
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:We present a framework for bi-level trajectory optimization in which a system's dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics representation enables constraint handling, additional variables, and non-smooth behavior to be abstracted away from the upper-level optimizer, and allows classical unconstrained optimizers to synthesize trajectories for more complex systems. We provide a path-following method for efficient evaluation of constrained dynamics and utilize the implicit-function theorem to compute smooth gradients of this representation. We demonstrate the framework by modeling systems from locomotion, aerospace, and manipulation domains including: acrobot with joint limits, cart-pole subject to Coulomb friction, Raibert hopper, rocket landing with thrust limits, and planar-push task with optimization-based dynamics and then optimize trajectories using iterative LQR.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3152696