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CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Whole-body optimization based control Abderrahmane K HEDDAR CNRS-AIST Joint Robotics Laboratory.

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Presentation on theme: "CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Whole-body optimization based control Abderrahmane K HEDDAR CNRS-AIST Joint Robotics Laboratory."— Presentation transcript:

1 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Whole-body optimization based control Abderrahmane K HEDDAR CNRS-AIST Joint Robotics Laboratory (JRL), UMI3218/CRT, Tsukuba, Japan CNRS-UM2 LIRMM, Interactive Digital Humans group, Montpellier, France

2 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Summary Motivation Multi-contact planning Dynamic motion generation between contact stances Whole-body dynamic optimization Experiments on HRP-2 Problems and open issues 2

3 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Cumbersome environments 3

4 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) 4 Escande, Kheddar, Miossec IEEE/RSJ IROS 2009 (video session)

5 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) PG: implementation 5

6 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Tasks in posture generation 6 9 can be used in a more general way to express tasks not related to planning: Orientation of a body looking at a target (including a new contact) keeping visual features in the field of view … It amounts to restraint to smaller sub-manifolds

7 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Example of tasks 77 Idea: having n collinear to k with the same direction Escande, Kheddar, Miossec, Garsault, ISER, 2008 Escande, Kheddar, IEEE/RSJ IROS 2009 Escande, Kheddar, Chapter 6 in Humanoid Motion Planning, K. Harada, E. Yoshida and K. Yokoi (Eds), Springer, STAR series, pp. 161–180, 2010 Carrying a glass

8 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Generalized PG Unifies manipulation and locomotion No distinctions Unifies objects, robots, agents Only goals are specified Functional extensions Bilateral contacts (e.g. grasps) Deformable bodies 8 Bouyarmane, Kheddar, IEEE/RSJ Humanoids, 2010 Bouyarmane, Kheddar, Multi-contact stances planning for multiple agents, IEEE ICRA, 2011

9 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Generalized PG in planning 9 Bouyarmane, Kheddar, Multi-contact stances planning for multiple agents, IEEE ICRA, 2011

10 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Motion generation Main ideas MPC on simplified models All variants of Kajita et al.’s PG Operational task-based prioritized control E.g. Sentis, Park, Khatib, IEEE TRO 2010 E.g. Mansard, Saab et al. or L. Righetti et al. ICRA 2011 Closed-loop QP-based control Computer Graphics communities (all variants) Bouyarmane et al. IROS 2011; Salini et al. ICRA 2011 Whole-body dynamic optimization This talk Possibly others E.g. learning techniques 10

11 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) 11 Benefits Minimization of a criteria Same method whatever the motion Easy inclusion of all constraints (actuator limitations, joint limits, stability, collision) Necessary for high performance motions, highly constrained motions Drawbacks Off-line (solution: motions database) Does not solve control problem (possibility : stochastic optimization) Why motion optimization

12 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) 12 Motion optimization problem System model General problem Look for a motion q(t) or control u(t) t in [0…t f ] Criteria to minimize f (q(t),u(t)) Constraints to satisfy c (q(t),u(t))</=0  t in [0…t f ] Problem to solve

13 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) 13 Solving method (first implementations) Discretization Of parameters q(t) = q(p,t) (ex.: B-Splines) Of constraints at times t i : c(q(t i )) </=0 i  [0…N] System control u(t) computed with inverse dynamic model Problem to solve Resolution with a nonlinear optimization algorithm How to solve optimization pb?

14 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) 14 General architecture of the program criteria Criteria gradient Constraints Constraints gradient k=0 k=k+1 Iteration k Convergence ? No Yes Definition of motion and constraints Motion class Criteria Criteria gradient Constraints Constraints gradient Optimization algorithm (IPOPT) k=0 k=k+1 Iteration k Convergence ? No Yes End B-Splines, dynamic model Implementation on HRP-2

15 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Optimal Motion Generation Optimal motion problematic Minimization of any criteria Energy consumption Time, jerk, etc. Constraints Actuators’ torque, max speeds, Joint limits… Collision and Auto-collision Unilateral contact, stability Output High performance desired motion with constraint satisfaction Tool Development of a software framework A unified constraint definition Miossec, Yokoi, Kheddar, IEEE ROBIO, 2006 Escande, Miossec, Kheddar, IEEE/RSJ Humanoids, 2007

16 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Dynamic transition from one feasible posture to another under joint torque limitation Combining two different motions accelerating an object upward sliding the body into under the object Arisumi, Chardonnet, Kheddar, Yokoi, IEEE ICRA 07 Arisumi, Miossec, Chardonnet, Yokoi, IEEE/RSJ IROS 08 Extreme tasks

17 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Problem Theoretical Difficult to find a compromise between the number of trajectory control points (optimization variables) and sampling time Difficult to keep a uniformed sampling when the final time is an optimization variable No guarantee of constraints satisfaction between time samples Optimization using interval analysis (Lengagne et al.) Guarantee of constraint satisfaction Computationally heavy 17

18 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Multi-contact motion optimization Whole-body model (incl. dynamics) Motion local planning Play trajectories in pseudo-closed-loop … if the solutions fits within critical time Use in a closed-loop scheme 18 Optimization Contact sets Dynamic motion

19 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Problem (case 1) 19

20 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Problem (case 2) 20

21 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Solution (adopted) 21 TfTf 0 t  k+1 (C i, C j ) t0t0 t5t5 t4t4 t1t1 t2t2 t3t3

22 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) More about subdivisions 22 Lengagne, Mathieu, Kheddar, Yoshida, HUMANOIDS, 2010 Lengagne, Mathieu, Kheddar, Yoshida, IROS, 2010

23 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Video: HRP-2 23 Reported by the NEW SCIENTIST and REUTERS Press Agency

24 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Other videos: HRP-2 24

25 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) HRP-2 emulating impaired leg 25 Lengagne, Kheddar, Druon, Yoshida, IEEE ROBIO, 2011

26 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Comparing motions 26

27 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Floating contact 27

28 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Avoiding collisions 28 Motion Parametrization Optimization Solver Polynomial Approximation (Taylor) Global Minima Distance Computation P

29 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) CD (no) 29

30 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) CD (yes) 30

31 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Conclusion Optimization is a powerful tool Yet, we need adding: Impact Multi-contact stabilizer Collision avoidance Faster solvers Flexibility of the ankle Current software status Draft 31

32 CNRS-AIST Joint Robotics Laboratory, UMI3218/CRT (JRL) Collaborators (on this topic) Past members New members (RoboHow.Cog) 32

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