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Composable Optimization for Robotics Simulation and Control.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Composable Optimization for Robotics Simulation and Control./
Author:	le Cleach, Simon Pierre Marie.
Description:	1 online resource (211 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Robotics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379658397

Composable Optimization for Robotics Simulation and Control.
le Cleach, Simon Pierre Marie.

Composable Optimization for Robotics Simulation and Control. - 1 online resource (211 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

Optimization is a fundamental part of robotics and can be seen in various aspects of the field, such as control and simulation. Both of these areas involve finding the best solutions to various optimization problems to achieve desired outcomes.Efficiency is key when it comes to solving these optimization problems. By finding solutions quickly and reliably, we can execute optimization-based controllers in realtime on hardware. The ability to quickly generate large amounts of simulation data is also valuable for offline optimization tasks such as policy optimization, co-design optimization, and system identification.Oftentimes, the optimization problems arising in robotics control and simulation have structure. Some problems directly fit into well-studied categories, for instance, the Linear Quadratic Regulator (LQR), other control problems can be cast as Linear Programs (LP), or Quadratic Programs (QP). For each of these categories there exist efficient and reliable solvers. Fitting your problem into one of these categories is often a safe strategy.However, there exist control and simulation tasks that involve complex optimization problems that do not fit these categories and for which there are currently no satisfactory solvers. In this dissertation, we focus on such problems. We are particularly interested in coupled optimization problems where the solution of one optimization problem is a parameter of another one.These coupled optimization problems can naturally arise in robotic simulation. For instance, the simulation of contact physics requires solving the least action principle and the maximum dissipation principle. We will see in Chapters 6 and 7 that we can solve these two optimization problems jointly. Coupled optimization problems also frequently arise in autonomous driving scenarios where agents are interacting. Indeed, each vehicle or pedestrian in the scene is optimizing its path to rally its destination as fast as possible while avoiding collisions.Conversely, we can deliberately choose to decompose a single complex optimization problem into a set of coupled optimization problems. We will see in Chapter 5 that decomposition is a strategy that can yield significant benefits in terms of the speed and reliability of the solver. In this context, optimization problems exchange gradient information by leveraging differentiable optimization. The strategy behind these choices is what we call composable optimization. In this dissertation, we focus on a few applications in robotics control and simulation namely game-theoretic control, control through contact, physics simulation, and collision detection. For these problems, we leverage composable optimization to exploit problem structure and devise efficient solvers. In some cases, we may combine multiple problems into a single optimization problem, while in other cases we may decompose the problems into simpler chunks. This approach allows us to tackle more complex optimization problems in a structured and efficient manner.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379658397Subjects--Topical Terms:

561941
Robotics.
Index Terms--Genre/Form:

554714
Electronic books.

Composable Optimization for Robotics Simulation and Control.
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500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Schwager, Mac;Kennedy, Monroe;Manchester, Zachary.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a Optimization is a fundamental part of robotics and can be seen in various aspects of the field, such as control and simulation. Both of these areas involve finding the best solutions to various optimization problems to achieve desired outcomes.Efficiency is key when it comes to solving these optimization problems. By finding solutions quickly and reliably, we can execute optimization-based controllers in realtime on hardware. The ability to quickly generate large amounts of simulation data is also valuable for offline optimization tasks such as policy optimization, co-design optimization, and system identification.Oftentimes, the optimization problems arising in robotics control and simulation have structure. Some problems directly fit into well-studied categories, for instance, the Linear Quadratic Regulator (LQR), other control problems can be cast as Linear Programs (LP), or Quadratic Programs (QP). For each of these categories there exist efficient and reliable solvers. Fitting your problem into one of these categories is often a safe strategy.However, there exist control and simulation tasks that involve complex optimization problems that do not fit these categories and for which there are currently no satisfactory solvers. In this dissertation, we focus on such problems. We are particularly interested in coupled optimization problems where the solution of one optimization problem is a parameter of another one.These coupled optimization problems can naturally arise in robotic simulation. For instance, the simulation of contact physics requires solving the least action principle and the maximum dissipation principle. We will see in Chapters 6 and 7 that we can solve these two optimization problems jointly. Coupled optimization problems also frequently arise in autonomous driving scenarios where agents are interacting. Indeed, each vehicle or pedestrian in the scene is optimizing its path to rally its destination as fast as possible while avoiding collisions.Conversely, we can deliberately choose to decompose a single complex optimization problem into a set of coupled optimization problems. We will see in Chapter 5 that decomposition is a strategy that can yield significant benefits in terms of the speed and reliability of the solver. In this context, optimization problems exchange gradient information by leveraging differentiable optimization. The strategy behind these choices is what we call composable optimization. In this dissertation, we focus on a few applications in robotics control and simulation namely game-theoretic control, control through contact, physics simulation, and collision detection. For these problems, we leverage composable optimization to exploit problem structure and devise efficient solvers. In some cases, we may combine multiple problems into a single optimization problem, while in other cases we may decompose the problems into simpler chunks. This approach allows us to tackle more complex optimization problems in a structured and efficient manner.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Robotics. $3 561941
650 4 $a Parameter estimation. $3 579804
650 4 $a Games. $3 595446
650 4 $a Sensitivity analysis. $3 1465552
650 4 $a Algorithms. $3 527865
650 4 $a Physics. $3 564049
655 7 $a Electronic books. $2 local $3 554714
690 $a 0605
690 $a 0771
710 2 $a Stanford University. $3 1184533
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 84-12B.
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