robert krug todor stoyanov achim j. lilienthal mobile...

Mobile Robotics and Olfaction Lab,

AASS, Örebro University

# 1 RSS 2015 July 16th 2015, Rome, Italy

Robert Krug Todor Stoyanov Achim J. Lilienthal robert.krug@oru.se todor.stoyanov@oru.se achim.lilienthal@oru.se

# 2 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Problem: relatively simple but fast autonomous pick & place and/or

manipulation

EU-FP7 RobLog: [Krug et al., ICRA, 2013], [Stoyanov et al., RAM, submitted]

Amazon Picking Challenge: winning team RBO

Applications: e. g. order picking or unloading in logistics scenarios

# 3 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Agenda

1. Grasp Synthesis- and Motion Planning

2. Exploiting Redundancy

3. Robust Grasp Execution

4. Preliminary Results

5. Discussion

# 4 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Grasp Synthesis-

and Motion Planning

1

# 5 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

1.

Grasp Synthesis- and Motion Planning

Where (synthesis) and how (motion planning) to grasp the target object?

Common solution: Sense-plan-act architecture; sampling-based planning

Grasp Synthesis: offline pre-computation

# 6 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

1.

Grasp Synthesis- and Motion Planning

Where (synthesis) and how (motion planning) to grasp the target object?

Common solution: Sense-plan-act architecture; sampling-based planning

Motion Planning: online with feasible – first execution [Berenson et al., Hummanoids, 2007], [Krug et al., ICRA, 2013]

# 7 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

1.

Grasp Synthesis- and Motion Planning

Where (synthesis) and how (motion planning) to grasp the target object?

Common solution: Sense-plan-act architecture; sampling-based planning

# 8 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

1.

Grasp Synthesis- and Motion Planning

Where (synthesis) and how (motion planning) to grasp the target object?

Common solution: Sense-plan-act architecture; sampling-based planning

Problems:

• Slow due to many futile motion planning attempts

• Difficult to incorporate prior knowledge

• Unnatural and/or sub-optimal trajectories

• Global but with probabilistic completeness

• Ill suited to incorporate contact events

• Ill suited to exploit redundancy

# 9 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Exploiting Redundancy

2

# 10 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

2.

Exploiting Redundancy

Where (synthesis) and how (motion planning) to grasp the target object?

Central Idea: Relax both problems by exploiting redundancy

Grasp representation as constraint envelopes

Grasp Synthesis

# 11 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

2.

Exploiting Redundancy

Where (synthesis) and how (motion planning) to grasp the target object?

Central Idea: Relax both problems by exploiting redundancy

Grasp representation tailored to constrained optimal control

reactive motion planning- and generation

Motion Planning

# 12 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Exploiting Redundancy

Where (synthesis) and how (motion planning) to grasp the target object?

Pros:

• Fast -> no planning delays

• Reactive

• Incorporate sensory feedback

• Incorporate prior knowledge

• Leverage redundancy

Suggested solution: constraint-based grasp- and environment description

and locally optimal motion control/generation

Cons:

• Approximated environment

• Local (depending on control)

# 13 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

2.

Exploiting Redundancy

OFFLINE ONLINE

Grasping Pipeline

define constraint

envelope templates perception

prune envelopes

motion generation

& control

robust grasp execution

# 14 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Robust Grasp Execution

3

# 15 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

3. Robust Grasp Execution

How about the introduced approximation and/or uncertainty errors?

Compensate with low pose sensitivity gripper [Tincani et al., IROS, 2012]

# 16 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Preliminary Results

4

# 17 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

4.

Preliminary Results

Local optimal control scheme

[Mansard, ICAR, 2009], [Kanoun, TRO, 2011]

# 18 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

4. Preliminary Results

KUKA Innovation Award Finalist: Advanced Picking & Palletizing (APPLE)

# 19 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

Discussion

5

# 20 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

5.

Discussion

Take away: Achieve fast and reactive grasping/manipulation behaviors by

exploiting redundancy via constraint-based grasp representations tailored

to optimal control schemes

Open Issues:

• “More global” solution would be nice MPC control

• Incorporate sensory feedback

• Learn from demonstrations and/or experience

• …

Mobile Robotics and Olfaction Lab,

AASS, Örebro University

# 21 RSS 2015 July 16th 2015, Rome, Italy

Robert Krug Todor Stoyanov Achim J. Lilienthal robert.krug@oru.se todor.stoyanov@oru.se achim.lilienthal@oru.se

# 22 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

References

6

# 23 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

6.

R. Krug, T. Stoyanov, M. Bonilla, V. Tincani, N. Vaskevicius, G. Fantoni, A. Birk, A. J. Lilienthal and A. Bicchi. Velvet Fingers: Grasp Planning and Execution for an Underactuated Gripper with Active Surfaces. Proc. of the IEEE Int. Conf. on Robotics and Automation, 2013, pp. 3669-3675.

T. Stoyanov, N. Vaskevicius, C. A. Mueller, T. Fromm, R. Krug, V. Tincani, R. Mojtahedzadeh, S. Kunaschk, R. Mortensen Ernits, D. R. Canelhas, M. Bonilla, S. Schwertfeger, M. Bonini, H. Halfar, K. Pathak, M. Rohde, G. Fantoni, A. Bicchi, A. Birk, A. J. Lilienthal and W. Echelmeyer. No More Heavy Lifting: Robotic Solutions to the Container Unloading Problem. IEEE Robotics & Automation Magazine, submitted, 2015.

D. Berenson, R. Diankov, K. Nishiwaki, S. Kagami and J. Kuffner Grasp Planning in Complex Scenes. Proc. of the IEEE/RAS Int. Conf. on Hummanoid Robots, 2007, pp. 42-48

References

# 24 © Robert Krug et al. @RSS – Grasp Envelopes for Constraint-based Robot Motion Planning and Control (Jul 16, 2015)

6.

V. Tincani, M. G. Catalano, E. Farnioli, M. Garabini, G. Grioli, G. Fantoni and A. Bicchi. Velvet Fingers: A Dexterous Gripper with Active Surfaces. Proc. of the IEEE/RAS Int. Conf. on Intelligent Robots and Systems, 2012, pp. 1257-1263.

N. Mansard, O. Stasse, P. Evrard and A. Kheddar. A Versatile Generalized Inverted Kinematics Implementation for Collaborative Working Humanoid Robots: The Stack of Tasks. Proc. of the Int. Conf. on Advanced Robotics, 2009, pp. 1-6.

O. Kanoun, F. Lamiraux and P.-B. Wieber Kinematic Control of Redundant Manipulators: Generalizing the Task-Priority Framework to Inequality Task. IEEE Transactions on Robotics, 2011, 27:4, pp. 785-792

References