functional gradient motion planning in reproducing kernel
play

Functional Gradient Motion Planning in Reproducing Kernel Hilbert - PowerPoint PPT Presentation

Jul 01, 2023 •305 likes •520 views

Functional Gradient Motion Planning in Reproducing Kernel Hilbert Spaces RSS Robotics Science and Systems June 18-26, 2016 University of Michigan Joint work with: Zita Marinho Geoffrey J. Gordon (CMU) zmarinho@cmu.edu Siddhartha Srinivasa

  1. Functional Gradient Motion Planning in Reproducing Kernel Hilbert Spaces RSS Robotics Science and Systems June 18-26, 2016 University of Michigan Joint work with: Zita Marinho Geoffrey J. Gordon (CMU) zmarinho@cmu.edu Siddhartha Srinivasa (CMU) Anca Dragan (UC Berkeley) Personal Robotics Lab Byron Boots (Georgia Tech) Institute for Systems and Robotics Arunkumar Byravan (University of Washington) CMU/Portugal

  2. Main Contribution all possible trajectories zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 2 / 7

  3. Main Contribution RKHS interpolated waypoints all possible trajectories zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 2 / 7

  4. Main Contribution RKHS interpolated waypoints all possible trajectories interpolated waypoints in RKHSs zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 2 / 7

  5. Waypoints trajectory ( CHOMP being used to grab a plate inside the microwave ) zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 3 / 7

  6. Waypoints trajectory ( CHOMP being used to grab a plate inside the microwave ) zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 3 / 7

  7. RKHS - Trajectory Reproducing Kernel Hilbert Space (RKHS) t 3 time points ∈ [0,1] t 1 t 2 zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 4 / 7

  8. RKHS - Trajectory Reproducing Kernel Hilbert Space (RKHS) t 3 time points ∈ [0,1] t 1 t 2 zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 5 / 7

  9. RKHS - Trajectory Reproducing Kernel Hilbert Space (RKHS) t 3 time points ∈ [0,1] t 1 t 2 zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 6 / 7

  10. RKHS - Trajectory Reproducing Kernel Hilbert Space (RKHS) t 3 time points ∈ [0,1] t 1 t 2 zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 7 / 7

  11. RKHS - Trajectory Reproducing Kernel Hilbert Space (RKHS) t 3 time points ∈ [0,1] t 1 t 2 zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 8 / 7

  12. Planning in RKHS - Optimization zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 9 / 7

  13. Planning in RKHS - Optimization zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 9 / 7

  14. Planning in RKHS - Optimization zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 9 / 7

  15. Planning in RKHS - Optimization zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 9 / 7

  16. Planning in RKHS - Optimization zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 9 / 7

  17. Results - RKHS require few iterations (c) Original CHOMP , smaller steps (a) RBF RKHS, large steps (b) Original CHOMP , large steps zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 10 / 7

  18. Results - 6D Robot arm : more complex scenes Waypoints Waypoints Waypoints Gaussian RBF derivative Gaussian RBF w/ joint interactions Gaussian RBF derivative Gaussian RBF Gaussian RBF w/ diagonal kernel Gaussian RBF 5 iterations 20 iterations 20 iterations - - - zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 11 / 7

  19. Planning in RKHS - RKHS derivative trajectory ( RKHS derivative kernel used to grab a plate inside the microwave ) zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 12 / 7

  20. Planning in RKHS - RKHS derivative trajectory ( RKHS derivative kernel used to grab a plate inside the microwave ) zmarinho@cmu.edu - Functional Gradient Motion Planning in RKHSs - RSS Robotics Science and Systems 2016 12 / 7

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Reproducing Kernel Hilbert Spaces Lorenzo Rosasco 9.520 Class 03 L. Rosasco RKHS About this

Reproducing Kernel Hilbert Spaces Lorenzo Rosasco 9.520 Class 03 L. Rosasco RKHS About this

Reproducing Kernel Hilbert Spaces Lorenzo Rosasco 9.520 Class 03 L. Rosasco RKHS About this class Goal To introduce a particularly useful family of hypothesis spaces called Reproducing Kernel Hilbert Spaces (RKHS) We will discuss several

541 views • 37 slides
Scalable Learning in Reproducing Kernel Kre n Spaces Dino Oglic 1 Thomas Grtner 2 1

Scalable Learning in Reproducing Kernel Kre n Spaces Dino Oglic 1 Thomas Grtner 2 1

Scalable Learning in Reproducing Kernel Kre n Spaces Dino Oglic 1 Thomas Grtner 2 1 Department of Informatics, Kings College London 2 School of Computer Science, University of Nottingham In Proceedings of the 36th International Conference

261 views • 7 slides
Reproducing Kernel Hilbert Spaces for Classification Katarina Domijan and Simon P. Wilson

Reproducing Kernel Hilbert Spaces for Classification Katarina Domijan and Simon P. Wilson

Reproducing Kernel Hilbert Spaces for Classification Katarina Domijan and Simon P. Wilson Department of Statistics, University of Dublin, Trinity College, Ireland November 1, 2005 1 Working Group on Statistical Learning General problem

428 views • 21 slides
Positive kernels and reproducing kernel spaces: a rich tapestry of settings and applications

Positive kernels and reproducing kernel spaces: a rich tapestry of settings and applications

Positive kernels and reproducing kernel spaces: a rich tapestry of settings and applications Joseph A. Ball Department of Mathematics, Virginia Tech, Blacksburg, VA joint work with Gregory Marx (Virginia Tech) and Victor Vinnikov (Ben Gurion

805 views • 41 slides
Counterfactual Policy Evaluation in Reproducing Kernel Hilbert Spaces Krikamol Muandet Max

Counterfactual Policy Evaluation in Reproducing Kernel Hilbert Spaces Krikamol Muandet Max

Counterfactual Policy Evaluation in Reproducing Kernel Hilbert Spaces Krikamol Muandet Max Planck Institute for Intelligent Systems Tbingen, Germany Jeju, Korea February 22, 2019 Krikamol Muandet Counterfactual Learning in RKHS Jeju,

1.14k views • 61 slides
Composition operators on some analytic reproducing kernel Hilbert spaces Jan Stochel (Uniwersytet

Composition operators on some analytic reproducing kernel Hilbert spaces Jan Stochel (Uniwersytet

Composition operators on some analytic reproducing kernel Hilbert spaces Jan Stochel (Uniwersytet Jagiello nski) Jerzy Stochel (AGH University of Science and Technology) Operator Theory and Operator Algebras 2016 December 13-22, 2016

1.44k views • 87 slides
Econ 2148, fall 2017 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines

Econ 2148, fall 2017 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines

Shrinkage Econ 2148, fall 2017 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines Maximilian Kasy Department of Economics, Harvard University 1 / 37 Shrinkage Agenda 6 equivalent representations of the posterior mean

742 views • 37 slides
Econ 2148, fall 2019 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines

Econ 2148, fall 2019 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines

Shrinkage Econ 2148, fall 2019 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines Maximilian Kasy Department of Economics, Harvard University 1 / 37 Shrinkage Agenda 6 equivalent representations of the posterior mean

854 views • 37 slides
Maxim Likhachev 1 Planning via Cell Decomposition Planning via Cell Decomposition Graph

Maxim Likhachev 1 Planning via Cell Decomposition Planning via Cell Decomposition Graph

Motion/Path Planning Motion/Path Planning Examples (of what is usually referred to as path planning): Examples (of what is usually referred to as motion planning): Planned motion for a 6DOF robot arm CSE 473:Planning in Robotics (slides

573 views • 3 slides
Algorithmic Robotics and Motion Planning Motion planning and arrangements I: General

Algorithmic Robotics and Motion Planning Motion planning and arrangements I: General

Algorithmic Robotics and Motion Planning Motion planning and arrangements I: General considerations Dan Halperin School of Computer Science Tel Aviv University Fall 2019-2020 Overview Arrangements, reminder Arrangements and

211 views • 19 slides
Feature-Sensitive Motion Planning Terra Horton http://parasol.tamu.edu/~tth4515 Motion Planning

Feature-Sensitive Motion Planning Terra Horton http://parasol.tamu.edu/~tth4515 Motion Planning

Feature-Sensitive Motion Planning Terra Horton http://parasol.tamu.edu/~tth4515 Motion Planning start The Basic concept of motion planning is to find a path that moves a robot (movable object) from a start configuration to a goal

298 views • 11 slides
and Motion Planning Sampling-based motion planning II: Single query planners and the RRT family

and Motion Planning Sampling-based motion planning II: Single query planners and the RRT family

Algorithmic Robotics and Motion Planning Sampling-based motion planning II: Single query planners and the RRT family Dan Halperin School of Computer Science Fall 2019-2020 Tel Aviv University Overview RRT bi-RRT Poor quality

429 views • 42 slides
and Motion Planning Multi robot motion planning: Extended review Dan Halperin School of Computer

and Motion Planning Multi robot motion planning: Extended review Dan Halperin School of Computer

Algorithmic Robotics and Motion Planning Multi robot motion planning: Extended review Dan Halperin School of Computer Science Fall 2019-2020 Tel Aviv University Alternative settings/approaches distributed, swarm the discrete version:

859 views • 54 slides
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations Bal

Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations Bal

Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations Bal azs Csan ad Cs aji & Kriszti an Bal azs Kis SZTAKI: Institute for Computer Science and Control MTA: Hungarian Academy of Sciences

510 views • 24 slides
CS 603 - Path Planning Rod Grupen 4/23/20 Robotics 1 4/23/20 Robotics 2 Why Motion

CS 603 - Path Planning Rod Grupen 4/23/20 Robotics 1 4/23/20 Robotics 2 Why Motion

Why Path Planning? CS 603 - Path Planning Rod Grupen 4/23/20 Robotics 1 4/23/20 Robotics 2 Why Motion Planning? Origins of Motion Planning GE Virtual Prototyping Character Animation T. Lozano-Prez and M.A. Wesley: Structural

310 views • 14 slides
LOCOMOTION Zeehyo Kim ( ) 2019.12.03 Recap 1. Ballistic Motion Planning

LOCOMOTION Zeehyo Kim ( ) 2019.12.03 Recap 1. Ballistic Motion Planning

CS686 MOTION PLANNING & APPLICATION PLANNER FOR MULTI-CONTACT LOCOMOTION Zeehyo Kim ( ) 2019.12.03 Recap 1. Ballistic Motion Planning Simulation for ballistic motion of point robot in 3D environment. Constraints from

596 views • 38 slides
AIDE: Augmented Onboarding of IoT Devices at Ease Huanle Zhang # , Mostafa Uddin & , Fang Hao

AIDE: Augmented Onboarding of IoT Devices at Ease Huanle Zhang # , Mostafa Uddin & , Fang Hao

AIDE: Augmented Onboarding of IoT Devices at Ease Huanle Zhang # , Mostafa Uddin & , Fang Hao & , Sarit Mukherjee & , Prasant Mohapatra # # University of California, Davis, California & Nokia Bell Labs, Murray Hill, New Jersey ACM

501 views • 10 slides
What makes a model linear IN TR OD U C TION TO L IN E AR MOD E L IN G IN P YTH ON Jason Vest

What makes a model linear IN TR OD U C TION TO L IN E AR MOD E L IN G IN P YTH ON Jason Vest

What makes a model linear IN TR OD U C TION TO L IN E AR MOD E L IN G IN P YTH ON Jason Vest u to Data Scientist Ta y lor Series Things to kno w: 1. appro x imate an y c u r v e 2. pol y nomial form : y = a0 + a1*x + a2*x**2 + a3*x**3 + ....

561 views • 33 slides
Time Series Regression A regression model relates a response x t to inputs z t, 1 , z t, 2 , .

Time Series Regression A regression model relates a response x t to inputs z t, 1 , z t, 2 , .

Time Series Regression A regression model relates a response x t to inputs z t, 1 , z t, 2 , . . . , z t,q : x t = 1 z t, 1 + 2 z t, 2 + + q z t,q + error . Time domain modeling: the inputs often include lagged val- ues of

367 views • 12 slides
Neutrino Fingerprints in Compact Objects Irene Tamborra Niels Bohr Institute, University of

Neutrino Fingerprints in Compact Objects Irene Tamborra Niels Bohr Institute, University of

Neutrino Fingerprints in Compact Objects Irene Tamborra Niels Bohr Institute, University of Copenhagen Compact Objects for All February 11, 2020 Neutrinos e Ghostly Abundant Elusive Ideal Messengers E scaping unimpeded,

517 views • 32 slides
Introduc tion to E c onome tric s Cha pte r 5 E ze quie l Urie l Jim ne z Unive rsity of Va

Introduc tion to E c onome tric s Cha pte r 5 E ze quie l Urie l Jim ne z Unive rsity of Va

Introduc tion to E c onome tric s Cha pte r 5 E ze quie l Urie l Jim ne z Unive rsity of Va le nc ia Va le nc ia , Se pte mbe r, 2013 5 Multiple re g re ssion a na lysis with qua lita tive informa tion 5.1 Introduc tion of qua lita tive

266 views • 22 slides
Evaluation of Memory and CPU usage via Cgroups of ATLAS workloads via Cgroups of ATLAS workloads

Evaluation of Memory and CPU usage via Cgroups of ATLAS workloads via Cgroups of ATLAS workloads

Evaluation of Memory and CPU usage Evaluation of Memory and CPU usage via Cgroups of ATLAS workloads via Cgroups of ATLAS workloads running at a Tier-2 running at a Tier-2 Gang Qin, Gareth Roy Ma March rch. 25 th , 2015 Condor Cgroups

363 views • 19 slides
State Abstraction Techniques for the Verification of Reactive Circuits Designing Correct

State Abstraction Techniques for the Verification of Reactive Circuits Designing Correct

Title Page State Abstraction Techniques for the Verification of Reactive Circuits Designing Correct Circuits, European Joint Conference on Theory and Practice of Software, Grenoble, France april 6-7 2002 Yannis Bres, CMA-EMP / INRIA

422 views • 20 slides
( x x )( x x ) 1 1 2 2 r r 0 if 12 21

( x x )( x x ) 1 1 2 2 r r 0 if 12 21

Lecture 17: Finish Review of EXAM I Chapter 16, 1 5 6 weeks left...12 lectures. I will cover at least chpt 6 11. Any spare time will be used in the lab. Lecture on Chapter 6 Specification: 1. Choosing the correct independent variables 2.

288 views • 12 slides

More recommend