iCub Whole-Body Control Through Force Regulation with ...€¦ · 06/08/2016 · Francesco Nori...

Francesco Nori IIT

iCub Facility Department

iCub Whole-Body Control Through Force

Regulation with Distributed Tactile Sensing

Francesco Nori IIT




Francesco Nori IIT




Francesco Nori IIT




Francesco Nori IIT




WHAT

Francesco Nori IIT




Francesco Nori IIT




WHAT

Francesco Nori IIT




HOW

⇥J(q) J(q)

⇤ vqvq

�+ J(q, vq) + J(q, vq) = 0

M(q)vq +C(q, vq)vq + g(q) =

0⌧q

�+ J>(q)fM(q)vq +C(q, vq)vq + g(q) =

0⌧q

�+ J>(q)f

M(q)vq + C(q, vq)vq + g(q) =0⌧q

�+ J>(q)fM(q)vq + C(q, vq)vq + g(q) =

0⌧q

�+ J>(q)f

Self

Other

Contact

Francesco Nori IIT




TechnologyHOW

G. Metta, L. Natale, G.Sandini, F. Nori

Science

S. Traversaro, F. Romano, D. Pucci, L.Fiorio, A. Del Prete, J. Eljaik, F. Nori

SoftwareRBCS: S. Traversaro, J. Eljaik,

ICUB: A. Cardellino, D. Domenichelli, G. Metta, L. Natale ADVR: A. Rocchi, M. Ferrati, E. Mingo Hoffman, A.

Settimi, N. Tsagarakis, A. Bicchi

Francesco Nori IIT




Science





TechnologyHOW


Francesco Nori IIT




Joint Torques and External Wrenches Estimation

- Goal: measure robot interaction with the world- Effect of motors : joint torques - Interaction with environment: external wrenches

- We have a specific set of sensors on the iCub- skin, six axis Force Torque sensors, IMU

- We exploited the classical RNEA, applied to the robot subtrees induced by the F/T sensors

Bartolozzi, C., Natale, L., Nori, F. and Metta, G. “Robots with a sense of touch” Nature Materials Vol. 15(9), pp. 921-925.

Measured wrench

Estimated joint torque

Estimated external wrench

Whole-body Tactile sensors

Six-axes force/torque sensors

Francesco Nori IIT




Robust real-time estimation

- Goal to estimate relevant dynamic variables (e.g. joint torques, contact wrenches)- robustly, through the computation of least-squares

solution;- in real-time, keeping the computational time below the

10ms;

F. Nori, N. Kuppuswamy and S. Traversaro, "Simultaneous state and dynamics estimation in articulated structures," (IROS 2015),F.Nori “Inverse, forward and other dynamic computations computationally optimized with sparse matrix factorizations" (RCAR 2017)

0 10 20 300

0.5

1

1.5

2

2.5 104

dMAP = argmax

dp(d|y),

⌃d|y =

�⌃

�1D + Y >

⌃

�1y Y

��1,

dMAP = ⌃d|y⇥Y >

⌃

�1y (y � by) + ⌃

�1D µD

⇤

- rigid body Newton-Euler equations as linear equations

- computational optimised solution finding the permutation that reduces the number of fill-in (for the robot configurations)

whole-bodyFT sensors

whole-bodyaccelerometers

whole-bodygyroscopes

Francesco Nori IIT


Interaction and its role as a building block for

artificial intelligence

Francesco Nori IIT







TechnologyHOW


Science




Francesco Nori IIT


Activity planOth

er c

om

plia

nce

PresentPast Future

Francesco Nori IIT




HOW


0⌧q


0⌧q

�+ J>(q)fSelf

Contact J(q)vq + J(q, vq) = 0

Francesco Nori IIT




Lyapunov stable momentum-based controller

G. Nava, F. Romano, F. Nori and D. Pucci, "Stability analysis and design of momentum-based controllers for humanoid robots," IROS 2016

Goal:

-provide a momentum-based controller with guaranteed Lyapunov stability.

-guarantee the stability of the zero-dynamics both theoretically and with validations on the real robot.

Control objectives

1) Stabilise the robot’s momentum

2) Stability of the zero dynamics

Control of center-of-mass

Control of joints

In its null space

M(q)⌫ + C(q, ⌫)⌫ + g(q)� JT f =

✓06⌧

◆ First 6 equations are the derivative of momentum

H =

P

LG

�= wL + wR +

mg0

�= Xf +

mg0

� External wrenches considered as input

Francesco Nori IIT






Goal:



Control objectives




Control of joints

In its null space

f⇤, || ˙H||2

minimize

f

�� ˙Hd � ˙H��

2

subject to

˙H = Xf +

mg0

�

friction constraints

center of pressure constraints

positivity of the normal forces.

Francesco Nori IIT






Goal:



Control objectives




Control of joints

In its null space

minimize

⌧k⌧ � 'k2

subject to M(q)⌫ + C(q, ⌫)⌫ + g(q) = B⌧ + J>f

J(q)⌫ +

˙J(q)⌫ = 0

f = f⇤

' = to be chosen

Francesco Nori IIT




Francesco Nori IIT




Integrability of Average Angular Velocity

A. Saccon, S. Traversaro, F. Nori, H. Nijmeijer "On Centroidal Dynamics and Integrability of Average Angular Velocity " RAL 2017

Goal:

- define an orientation frame that depends only on the current robot configuration

- Candidate: integral of the average angular velocity

- Conclusion: such an orientation frame does not exist

6

covariant derivative of F with respect to the (0) Cartan-Schouten connection [31], which is known to be symmetric,to obtain the following expression, similar to (25) but nowcoordinate independent,

✓@Ai

@sj(s) +

1

2

Ai(s)⇥Aj(s)

◆^F (s) , (35)

which leads then again to (20). Principal connections havebeen also employed to study nonholonomic locomotion, asthe nonholomic constraint of a robot can be written in a formequivalent to (22): see, e.g., [32]. We hope the presentationgiven in this paper will help also accessing that literature.

C. The link between locked and average velocity

In this subsection, we elaborate further on the remark givenin the subsection IV-A showing that, when choosing AoC =

Apcom, the centroidal kinematic is simply given by

AoC =

Apcom (36)A˙RC =

A!^loc

ARC (37)

with A!loc given by the angular velocity component of Avloc.

We first show that, independently from where AoC is located,Gvave and A

vloc satisfyApcomA!loc

�=

Gvave =

GXAAvloc. (38)

This shows that the average angular velocity G!ave coin-cides with the locked angular velocity A!loc. Proving (38)is obtained by employing the remark in the subsection IV-Aregarding the invariance of the coordinates of the CoM andthe following lemma.Lemma. Given the differential equation

A˙HC =

Av

^ AHC (39)

assume there is a point p such that its coordinates Cp withrespect to C are constant. Define G = (p, [A]) so that G hasp as origin and the same orientation of A. Then, the velocityAv written with respect to G equals

Gv =

GXAAv =

ApA!

�(40)

where A! denotes the angular velocity component of Av. ⌅

Finally, to obtain (36)-(37), we employ (38) to express Avloc

in terms of Gvave and substitute it into (17), obtaining

AoC =

Apcom +

A!^loc(

AoC � Apcom) (41)A˙RC =

A!^loc

ARC . (42)

where we recall AHC = (

ARC ,AoC ; 01⇥3

, 1) 2 SE(3). Aswe have selected AoC =

Apcom, the result follows.

Fig. 1. The free-floating three link model. In this figure, s1 and s2 representthe relative orientations of the two distal links with respect to the base. Seemain text for a full description of the figure.

V. A NUMERICAL EXAMPLE

In this section, a simple example to illustrate the use of theintegrability condition (20) is given. We consider a mechanismwith two internal DOFs. This is the minimal number of DOFsto observe the nonintegrability, because, for one DOF, (20) isalways trivially satisfied.

We numerically integrate (17), performing a motion thatstarts and ends at the same internal joint configuration. Thebase link will not, in general, return to the original pose. Thecentroidal frame will always return to the original orientationrelative to the base link, if and only if (20) holds. In bothcases, as explained in the Remark of Section IV-A, its CoMwill return to its original position.

An illustration of the mechanism is given in Figure 1. Themechanism is composed by three rigid bodies: a free-floatingbase link (yellow) and two distal links (cyan and magenta).

The distal links are connected directly to the base via twoindependently actuated revolute joints. For both links, theoffset between their center of mass and joint axis is identicaland denoted with d. To each body we firmly attach threecoordinate frames, indicated as B, 1 and 2 in the figure, eachcentered at the corresponding body’s CoM. The base link massis 1 kg. Each distal link mass is also 1 kg. For the base link,the rotational inertia (about the axis passing through the CoMand orthogonal to the base link face) is 4 kg m2. For distallinks, the inertia is 1 kg m2. The rotational inertia with respectto the other directions are non influential (we are consideringa planar mechanism) and can be assigned arbitrarily findingthe same result provided below.

We verify (20) for two different values of d: namely, ford = 1 and d = 0. For d = 1, B

12

= �B21

is equal, up to adivision by the factor (2C

1�2

+ 6S1

� 6S2

� 28)

2, to2

6666664

2(C1+C2) (4C1+4C2�3C1 S2+3C2 S1)

2C1 (4S1+4S2�3S1 S2�3S2S2)+2C2 (4S1+4S2+3S1 S2+3S1S1)

0

0

0

�18S1�2�24C1�24C2

3

7777775

where S1

:= sin(s1

), S2

:= sin(s2

), C1

:= cos(s1

), C2

:=

cos(s2

), S1�2

:= sin(s1

� s2

), and C1�2

:= cos(s1

� s2

).For d = 0, B

12

= B21

⌘ 0. Details of the straightforward buttedious computations are not provided for space limitations.

The conclusion is that, just for d = 0, the integration ofthe average angular velocity will always produce a centroidalframe whose orientation is only a function of the internal joint

8

t = 0 s t = 2 s t = 4 s t = 6 s t = 8 s t = 10 sFig. 2. Evolution of the centroidal frame. Nonintegrable case with d = 1 (first row). Integrable case with d = 0 (second row)

fact that the momentum map J expressed in the G frame isgiven by

GJ (H, s, v, s) = GXBBLB

Bvloc

= GLGGvloc

where GLG is block diagonal with first block on the diagonalequal to mI

3⇥3

with m the total mass and that the linearmomentum component of GJ is necessarily mApcom.

REFERENCES

[1] P. M. Wensing and D. E. Orin, “Improved computation of thehumanoid centroidal dynamics and application for whole-body control,”International Journal of Humanoid Robotics, vol. 13, no. 01, p.1550039, 2016. [Online]. Available: http://www.worldscientific.com/doi/abs/10.1142/S0219843615500395

[2] G. Nava, F. Romano, F. Nori, and D. Pucci, “Stability analysis anddesign of momentum-based controllers for humanoid robots,” in 2016IEEE/RSJ International Conference on Intelligent Robots and Systems,Oct 2016.

[3] T. Koolen, S. Bertrand, G. Thomas, T. De Boer, T. Wu, J. Smith,J. Englsberger, and J. Pratt, “Design of a momentum-based controlframework and application to the humanoid robot atlas,” InternationalJournal of Humanoid Robotics, vol. 13, 2016.

[4] G. Garofalo, B. Henze, J. Englsberger, and C. Ott, “On the inertiallydecoupled structure of the floating base robot dynamics,” 8th ViennaInternational Conference on Mathematical Modelling (MATHMOD), pp.322–327, 2015.

[5] H. Dai, A. Valenzuela, and R. Tedrake, “Whole-body motion planningwith centroidal dynamics and full kinematics,” in 2014 IEEE-RASInternational Conference on Humanoid Robots, 2014, pp. 295–302.

[6] C. Ott, M. A. Roa, and G. Hirzinger, “Posture and balance control forbiped robots based on contact force optimization,” in Humanoid Robots(Humanoids), 2011 11th IEEE-RAS International Conference on. IEEE,2011, pp. 26–33.

[7] S.-H. Lee and A. Goswami, “A momentum-based balance controller forhumanoid robots on non-level and non-stationary ground,” AutonomousRobots, vol. 33, no. 4, pp. 399–414, 2012.

[8] S. Kajita, F. Kanehiro, K. Kaneko, F. Fujiwara, K. Harada, K. Yokoi, andH. Hirukawa, “Resolved momentum control: Humanoid motion planningbased on the linear and angular momentum,” in IEEE InternationalConference on Intelligent Robots and Systems, 2003, pp. 1644–1650.

[9] D. E. Orin, A. Goswami, and S.-H. Lee, “Centroidal dynamics of ahumanoid robot,” Auton. Robots, vol. 35, no. 2-3, pp. 161–176, Jun.2013.

[10] J. E. Marsden, Lectures on Mechanics. Cambridge University Press,1992.

[11] A. M. Bloch, P. Krishnaprasad, J. E. Marsden, and R. M. Murray, “Non-holonomic mechanical systems with symmetry,” Archive for RationalMechanics and Analysis, vol. 136, no. 1, pp. 21–99, 1996.

[12] R. Featherstone, Rigid body dynamics algorithms. Springer, 2008.[13] R. Featherstone and D. E. Orin, “Dynamics,” in Springer Handbook of

Robotics, 2nd Ed, B. Siciliano and O. Khatib, Eds., 2016.[14] A. Bloch, Nonholonomic Mechanics and Control, with the collaboration

of J. Ballieul, P. Crouch, and J.Marsden. Springer-Verlag, 2003.

[15] J. E. Marsden and T. Ratiu, Introduction to mechanics and symmetry: abasic exposition of classical mechanical systems. Springer Science &Business Media, 2013, vol. 17.

[16] R. M. Murray, Z. Li, S. S. Sastry, and S. S. Sastry, A mathematicalintroduction to robotic manipulation. CRC press, 1994.

[17] F. C. Park, J. Bobrow, and S. R. Ploen, “A Lie group formulation ofrobot dynamics,” International Journal of Robotic Research, vol. 14,no. 6, pp. 609–618, 1995.

[18] S. Traversaro and A. Saccon, “Multibody Dynamics Notation,”Technische Universiteit Eindhoven, Tech. Rep., 2016. [Online].Available: http://repository.tue.nl/849895

[19] T. De Laet, S. Bellens, R. Smits, E. Aertbelien, H. Bruyninckx, andJ. De Schutter, “Geometric relations between rigid bodies (part 1):Semantics for standardization,” Robotics & Automation Magazine, IEEE,vol. 20, no. 1, pp. 84–93, 2013.

[20] H. Bruyninckx and J. De Schutter, “Symbolic differentiation of thevelocity mapping for a serial kinematic chain,” Mechanism and machinetheory, vol. 31, no. 2, pp. 135–148, 1996.

[21] A. Jain, Robot and Multibody Dynamics: Analysis and Algorithms.Springer, 2010.

[22] B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: modelling,planning and control. Springer Science & Business Media, 2010.

[23] S. Chiaverini, G. Oriolo, and I. D. Walker, “Kinematically redundantmanipulators,” in Springer handbook of robotics. Springer, 2008, pp.245–268.

[24] P.-B. Wieber, R. Tedrake, and S. Kuindersma, “Modeling and control oflegged robots,” in Springer Handbook of Robotics, 2nd Ed, B. Sicilianoand O. Khatib, Eds., 2016.

[25] J. E. Marsden and J. Scheurle, “The reduced euler-lagrange equations,”Fields Institute Comm, vol. 1, pp. 139–164, 1993.

[26] K. R. Ball, D. V. Zenkov, and A. M. Bloch, “Variational structures forhamel’s equations and stabilization,” IFAC Workshop on Lagrangian andHamiltonian Methods for Non Linear Control, pp. 178–183, 2012.

[27] D. E. Orin and A. Goswami, “Centroidal momentum matrix of a hu-manoid robot: Structure and properties,” in 2008 IEEE/RSJ InternationalConference on Intelligent Robots and Systems. IEEE, 2008.

[28] X. Ding and H. Chen, “Dynamic modeling and locomotion controlfor quadruped robots based on center of inertia on SE(3),” Journal ofDynamic Systems, Measurement, and Control, vol. 138, no. 1, 2016.

[29] P. Wieber, “Holonomy and nonholonomy in the dynamics of articulatedmotion,” in Fast Motions in Biomechanics and Robotics. Optimizationand Feedback Control, M. Diehl and K. Mombaur, Eds. Springer, 2005.

[30] S. Kobayashi and K. Nomizu, Foundations of differential geometry (Vol.I), 2nd ed. Wiley, 1996.

[31] A. Saccon, J. Hauser, and A. P. Aguiar, “Optimal control on lie groups:The projection operator approach,” IEEE Transactions on AutomaticControl, vol. 58, no. 9, pp. 2230–2245, 2013.

[32] J. Ostrowski and J. Burdick, “The geometric mechanics of undulatoryrobotic locomotion,” The International Journal of Robotics Research,vol. 17, pp. 683–701, 1998.

[33] J. W. Grizzle, G. Abba, and F. Plestan, “Asymptotically stable walkingfor biped robots: analysis via systems with impulse effects,” IEEETransactions on Automatic Control, vol. 46, no. 1, pp. 51–64, 2001.

[34] J. W. Grizzle, C. Chevallereau, R. W. Sinnet, and A. D. Ames, “Models,feedback control, and open problems of 3D bipedal robotic walking,”Automatica, vol. 50, no. 8, pp. 1955 – 1988, 2014.

Francesco Nori IIT




HOW


0⌧q


0⌧q

�+ J>(q)fSelf

Switching Contact Jt(q)vq + Jt(q, vq) = 0

Francesco Nori IIT




-

Walking on partial footholds

G. Wiedebach et al. ”Walking on Partial Footholds Including Line Contacts with the Humanoid Robot Atlas" Humanoids 2016.

Goal:

- Improved whole-body algorithm to balance with limited footholds

-Haptic exploration algorithm to refine the support polygon estimation

Expressing the CoP of a single foot in terms of the contact forces allows us to add a desired CoP of a foot into the optimization based controller.

Francesco Nori IIT




-

Walking on partial footholds

G. Wiedebach et al. ”Walking on Partial Footholds Including Line Contacts with the Humanoid Robot Atlas" Humanoids 2016.

Goal:

- Improved whole-body algorithm to balance with limited footholds

-Haptic exploration algorithm to refine the support polygon estimation

1. Shift the Center of Pressure (CoP) around in a foothold.

2. Observe the tracking of the CoP.3. Observe foot rotations.4. Refine the area of support by

cutting away parts that can not support weight.

Francesco Nori IIT


Dynamic Interaction Control

Francesco Nori IIT




-

Reference spreading for motions with impacts

M. Rijnen et al. "Control of Humanoid Robot Motions with Impacts: Numerical Experiments with Reference Spreading Control " ICRA 2017

Goal:

-perform dynamic motions which involve impacts;

-control trajectories which involve non-zero velocity contacts.

Session WeC8 Rm. 4613/4713 Wednesday, May 31, 2017, 14:30–15:45Humanoid Robots 2Chair Katja Mombaur, Heidelberg UniversityCo-Chair Tomomichi Sugihara, Graduate School of Engineering, Osaka University

2017 IEEE International Conference on Robotics and Automation

14:30–14:35 WeC8.1

Real-Time Pursuit-Evasion with Humanoid Robots

•A pursuer heads for collision with an evader that tries to escape •Both robots are controlled by a fast replanning scheme based on vision •Trajectory generation uses a feedback-controlled unicycle as template model •Simulations and experiments with NAOs reveal a limit cycle behavior

M. Cognetti, D. De Simone, F. Patota, N. Scianca, L. Lanari, G. Oriolo Sapienza University of Rome, Italy

14:35–14:40 WeC8.2

Passivity-based Control of Underactuated Biped Robots within Hybrid Zero Dynamics Approach

• A passivity-based controller for a planar biped with one degree of underactuation is designed within HZD approach.

• It is aimed to preserve the natural dynamics of the system in the transverse dynamics in contrast to input-output linearization method which cancels these dynamics.

• The asymptotic stability of the periodic orbit in lower dimensional state space is extended to the full dimensional space by a Lyapunov stability analysis of the full-order system.

Hamid Sadeghian1, Christian Ott2, Gianluca Garofalo2, and Gordon Cheng3

1Engineering Department, University of Isfahan, Isfahan, Iran 2Institute of Robotics and Mechatronics, DLR, Germany

3Institute for Cognitive Systems, Faculty of Electrical Engineering and Information Technology, Technical University of Munich, Munich, Germany

A 7-link planar underactuated system with zero ankle torque in stance foot.

14:40–14:45 WeC8.3

Control of Humanoid Robot Motions with Impacts: Numerical Experiments with Reference

Spreading Control

• Reference spreading stabilizes desired robot motions with impacts

• The reference motion is extended about impact times to create multiple segments

• Switching between segments occurs when a contact transition is detected

• Validation: balancing on one foot while impacting a wall with a hand

M Rijnen, E de Mooij, N v/d Wouw, A Saccon, H Nijmeijer Dept. of Mechanical Engineering, TU/e, The Netherlands

S Traversaro and F Nori iCub facility, IIT Genova, Italy

14:45–14:50 WeC8.4

Dynamic Multi-contact Transitions for Humanoid Robots using Divergent Component of Motion

• The motion planner computes feasible step durations and timing of contact transitions

• The Center of Mass trajectory has an analytical form

• A feasible solution is found within seconds by using a simplified robot model

• The controller combines a passivity-based approach with Divergent Component of Motion control.

George Mesesan Johannes Englsberger Bernd Henze Christian Ott

German Aerospace Center (DLR), Germany

14:50–14:55 WeC8.5

Smooth-Path-Tracking Control of a Biped Robot at Variable Speed Based on Dynamics Morphing

• A biped control to follow an arbitrary smooth curved path is proposed • The controller is designed with respect to a moving frame fixed to the

robot • Advantageous features include: (i) The referential path can be given as an arbitrary smooth curve (ii) The motion references can be given from the first-person viewpoint of the robot (iii) The states can be observed from the egocentric frame of reference for the robot

Hiroshi Atsuta1, Haruki Nozaki2 and Tomomichi Sugihara1 1 Grad. School of Enginerring, Osaka University, Japan

2 Yamazaki Mazak Corporation, Japan

14:55–15:00 WeC8.6

Experimental Evaluation of Deadbeat Running on the ATRIAS Biped

• We investigate how well control strategies developed for the theoretical spring mass model transfer to real-world bipedal robots.

• Our controller regulates running using model-based force control during the stance phase and deadbeat foot placement during flight.

• We find that our controller produces one-step, deadbeat tracking of velocity changes up to ±0.2 m/s at speeds up to 1.0 m/s.

• The control tolerates larger velocity changes, higher speeds, and ground height changes up to ±15 cm, but requires more steps.

William Martin, Albert Wu, and Hartmut Geyer The Robotics Institute, Carnegie Mellon University, USA

ATRIAS, a human-scale bipedal robot with no

external sensing

Francesco Nori IIT




-

Reference spreading for motions with impacts

M. Rijnen et al. "Control of Humanoid Robot Motions with Impacts: Numerical Experiments with Reference Spreading Control" ICRA 2017

Goal:

-perform dynamic motions which involve impacts;

-control trajectories which involve non-zero velocity contacts.

Paper WeC8.3, 14:30-15:45 Rm. 4613/4713 Regular Session WeC8

Session WeC8 Rm. 4613/4713 Wednesday, May 31, 2017, 14:30–15:45Humanoid Robots 2Chair Katja Mombaur, Heidelberg UniversityCo-Chair Tomomichi Sugihara, Graduate School of Engineering, Osaka University

2017 IEEE International Conference on Robotics and Automation

14:30–14:35 WeC8.1

Real-Time Pursuit-Evasion with Humanoid Robots

•A pursuer heads for collision with an evader that tries to escape •Both robots are controlled by a fast replanning scheme based on vision •Trajectory generation uses a feedback-controlled unicycle as template model •Simulations and experiments with NAOs reveal a limit cycle behavior

M. Cognetti, D. De Simone, F. Patota, N. Scianca, L. Lanari, G. Oriolo Sapienza University of Rome, Italy

14:35–14:40 WeC8.2

Passivity-based Control of Underactuated Biped Robots within Hybrid Zero Dynamics Approach

• A passivity-based controller for a planar biped with one degree of underactuation is designed within HZD approach.

• It is aimed to preserve the natural dynamics of the system in the transverse dynamics in contrast to input-output linearization method which cancels these dynamics.

• The asymptotic stability of the periodic orbit in lower dimensional state space is extended to the full dimensional space by a Lyapunov stability analysis of the full-order system.

Hamid Sadeghian1, Christian Ott2, Gianluca Garofalo2, and Gordon Cheng3

1Engineering Department, University of Isfahan, Isfahan, Iran 2Institute of Robotics and Mechatronics, DLR, Germany

3Institute for Cognitive Systems, Faculty of Electrical Engineering and Information Technology, Technical University of Munich, Munich, Germany

A 7-link planar underactuated system with zero ankle torque in stance foot.

14:40–14:45 WeC8.3

Control of Humanoid Robot Motions with Impacts: Numerical Experiments with Reference

Spreading Control

• Reference spreading stabilizes desired robot motions with impacts

• The reference motion is extended about impact times to create multiple segments

• Switching between segments occurs when a contact transition is detected

• Validation: balancing on one foot while impacting a wall with a hand

M Rijnen, E de Mooij, N v/d Wouw, A Saccon, H Nijmeijer Dept. of Mechanical Engineering, TU/e, The Netherlands

S Traversaro and F Nori iCub facility, IIT Genova, Italy

14:45–14:50 WeC8.4

Dynamic Multi-contact Transitions for Humanoid Robots using Divergent Component of Motion

• The motion planner computes feasible step durations and timing of contact transitions

• The Center of Mass trajectory has an analytical form

• A feasible solution is found within seconds by using a simplified robot model

• The controller combines a passivity-based approach with Divergent Component of Motion control.

George Mesesan Johannes Englsberger Bernd Henze Christian Ott

German Aerospace Center (DLR), Germany

14:50–14:55 WeC8.5

Smooth-Path-Tracking Control of a Biped Robot at Variable Speed Based on Dynamics Morphing

• A biped control to follow an arbitrary smooth curved path is proposed • The controller is designed with respect to a moving frame fixed to the

robot • Advantageous features include: (i) The referential path can be given as an arbitrary smooth curve (ii) The motion references can be given from the first-person viewpoint of the robot (iii) The states can be observed from the egocentric frame of reference for the robot

Hiroshi Atsuta1, Haruki Nozaki2 and Tomomichi Sugihara1 1 Grad. School of Enginerring, Osaka University, Japan

2 Yamazaki Mazak Corporation, Japan

14:55–15:00 WeC8.6

Experimental Evaluation of Deadbeat Running on the ATRIAS Biped

• We investigate how well control strategies developed for the theoretical spring mass model transfer to real-world bipedal robots.

• Our controller regulates running using model-based force control during the stance phase and deadbeat foot placement during flight.

• We find that our controller produces one-step, deadbeat tracking of velocity changes up to ±0.2 m/s at speeds up to 1.0 m/s.

• The control tolerates larger velocity changes, higher speeds, and ground height changes up to ±15 cm, but requires more steps.

William Martin, Albert Wu, and Hartmut Geyer The Robotics Institute, Carnegie Mellon University, USA

ATRIAS, a human-scale bipedal robot with no

external sensing

Francesco Nori IIT






Francesco Nori IIT


Activity planOth

er c

om

plia

nce

PresentPast Future

Francesco Nori IIT




HOW

⇥J(q) J(q)

⇤ vqvq

�+ J(q, vq) + J(q, vq) = 0


0⌧q


0⌧q

�+ J>(q)fSelf

Other

Contact

M(q)vq + C(q, vq)vq + g(q) = J>(q)f

Francesco Nori IIT

Robotics, Brain and Cognitive Sciences

iCub Whole-Body Control Through Force Regulation on Rigid Non-Coplanar Contacts

The outer loop: balancing on seesaw

- External wrenches on the robot controlled to stabilise a desired robot’s momentum

- Redundancy of external wrenches used for controlling seesaw and joint torque minimisation

- Actuation redundancy used to stabilise a desired robot’s “internal” configuration

- Robot Dynamics

- Seesaw dynamics

- Constraints

M(q)⌫ + h(q, ⌫) � J>(q)w = S⌧

JF (q)⌫ = VF VP = 0

Hs = mg � Aw + wP



Francesco Nori IIT


Activity planOth

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PresentPast Future

Francesco Nori IIT




HOW

⇥J(q) J(q)

⇤ vqvq

�+ J(q, vq) + J(q, vq) = 0


0⌧q


0⌧q

�+ J>(q)f

M(q)vq + C(q, vq)vq + g(q) =0⌧q

�+ J>(q)fM(q)vq + C(q, vq)vq + g(q) =

0⌧q

�+ J>(q)f

Self

Other

Contact

Francesco Nori IIT

Robotics, Brain and Cognitive Sciences

Interaction and its role as a building block for

artificial intelligence

Francesco Nori IIT


Dynamic Interaction Control

Ph.D.

candid

ates

PostDo

csMas

ter

studen

ts

D. Pucci F. Romano

M. Charbonneau F. Andrade C. Latella S. Dafarra G. Nava

M. Lazzaroni M. Lorenzini

Tenur

e trac

k

rese

arch

erF. Nori

S. Traversaro

N. Guedelha

Tech

nic

ian

D. Ferigo

Sponso

rs

Francesco Nori IIT




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