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Bioinspired grasping in Soft Robotics University of Hamburg Faculty - - PowerPoint PPT Presentation

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MIN Faculty Department of Informatics Bioinspired grasping in Soft Robotics University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of Multimodal Systems 11. November 2019

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MIN Faculty Department of Informatics

Bioinspired grasping in Soft Robotics

University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of Multimodal Systems

  • 11. November 2019

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 1 / 30

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Outline

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

  • 1. Motivation
  • 2. Grasping: Definition & Basics
  • 3. Charakteristics of Soft Robotics
  • 4. Grasp Synthesis
  • 5. Action-conditional model
  • 6. Conclusion
  • 7. References

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 2 / 30

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Motivation

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [1]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 3 / 30

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Grasping - Problem Outline

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ N "fingers" on the grasping device ◮ ⇒ N contact points to the object ◮ How is the right Grasping Pose calculated?

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 4 / 30

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Forces involved in grasping processes [3]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Normal force

iwn

Source:[2]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 5 / 30

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Forces involved in grasping processes [3]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Normal force iwn ◮ Tangential force

iwt

Source: [2]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 6 / 30

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Forces involved in grasping processes [3]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Normal force iwn ◮ Tangential force iwt ◮ Torsional moment

iwθ

Source:[4]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 7 / 30

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Contacts [3]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Frictionless contact: iwn ◮ Frictional contact: iwn ∧ iwt ◮ Soft contact: iwn ∧ iwt ∧ iwθ

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 8 / 30

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Equilibrium Grasp - Definition [3]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

A grasp is considered in equilibrium when: Wc + g = 0, c = 0 ◮ W := Wrench matrix ◮ c := Wrench intensity vector ◮ g := External wrench

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 9 / 30

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Force-closed Grasp - Definition [3]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

A grasp is considered to be force-closed, when for every wrench ˆ w there is an λ that fits the constraints of a equilibrium grasp so that: W λ = ˆ w ◮ Note: Every force-closed grasp is a stable grasp

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 10 / 30

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Categorization of Grasps

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Classes of grasps based on [3]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 11 / 30

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Properties [3]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Properties of the grasp process:

◮ Dexterity ◮ Equilibrium ◮ Stability ◮ Dynamic behaviour

◮ Problems in grasping:

◮ Slipping detection ◮ Fracture of grasped object

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 12 / 30

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Charakteristics of Soft Robotics [5]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Humanoid Soft Robotics

◮ Skeletton

◮ Metal ◮ Synthetic polymer

◮ Soft "skin" out of:

◮ Active elastomer ◮ Hydrogel ◮ Shape memory polymers ◮ e.g. GelSight, Dragon Skin, uSkin

◮ Animal-inspired Soft Robotics

◮ "CAN" be completely out of soft material

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 13 / 30

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Charakteristics of Soft Robotics: Examples

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [6] Source: [5] Source: [7]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 14 / 30

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Grasp Synthesis [8]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Two kinds of approaches:

◮ Analytical

◮ Objective: Calculate possibly best configuration of position and angles ◮ Constrained optimization problems ◮ Based on 3D-models

◮ Data-driven

◮ Objective: Reusing existing grasp experience ◮ Heuristic ◮ Knowledge-based

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 15 / 30

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Sensors [9]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Visual

◮ Depth sensing ◮ Pattern recognition ◮ ⇒ Stereo Camera Sensor

◮ Tactile

◮ Force sensing ◮ Surface exploration ◮ Slipping detection ◮ ⇒ GelSight, TacTip, etc.

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 16 / 30

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Action-conditional model [10]

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [10] . . ◮ Objective: Combine visual and tactile sensing ◮ Sensors: GelSight tactile sensor, Microsoft Kinect v2.0 ◮ Operating with raw input data ◮ Self supervised Deep Learning approach to predict grasp success ◮ Adjusting grasps (Regrasping) ◮ Optimizable for gentle grasps

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 17 / 30

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2D tactile-sensor input

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [10]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 18 / 30

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Calculation of success probability

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [10]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 19 / 30

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Convolutional Neural Network

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [11]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 20 / 30

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Multi-layer Perceptron

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [12]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 21 / 30

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Regrasp Optimization

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Source: [10]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 22 / 30

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Results

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Absolute sucessful Grasps

Source: [10]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 23 / 30

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Results

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Predicted success in relation to the applied force

Source: [10]

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 24 / 30

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Outlook

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

Multi-finger hand with uSkin:

Source: [9]

◮ Recognizing objects based on tactile sensing with 95% success

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 25 / 30

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Conclusion

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

◮ Soft Robotics are supportive for dynamic grasping tasks ◮ Visuo-tactile sensing is highly valuable for future grasping research ◮ But, more research is needed on:

◮ The combination of visual and tactile data ◮ Tactile sensors ◮ Suitable learning models for grasping

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 26 / 30

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References

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

[1] “Plen2 is a do-it-yourself coffee-carrying robot | Trusted Reviews.” [Online]. Available: https://www.trustedreviews.com/news/ plen2-is-a-do-it-yourself-coffee-carrying-robot-2933704 [2] “Force - Wikipedia.” [Online]. Available: https://en.wikipedia.org/wiki/Force [3]

  • W. S. Howard and V. Kumar, “On the stability of grasped
  • bjects,” IEEE Transactions on Robotics and Automation,
  • vol. 12, no. 6, pp. 904–917, 1996.

[4] “Torsionsmoment – Wikipedia.” [Online]. Available: https://de.wikipedia.org/wiki/Torsionsmoment

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 27 / 30

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References (cont.)

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

[5]

  • R. Xu, F. Sui, G. Jalan, P. Lee, L. Ren, M. Sanghadasa, and
  • L. Lin, “UNTETHERED SOFT ROBOTS WITH

BIOINSPIRED BONE-AND-FLESH CONSTRUCTS FOR FAST DETERMINISTIC ACTUATION Department of Mechanical Engineering , University of California , Berkeley , USA Aviation and Missile Research , Development , and Engineering Center , US A,” 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), no. June, pp. 1–4, 2019. [6]

  • M. Manti, T. Hassan, G. Passetti, N. D’Elia, C. Laschi, and
  • M. Cianchetti, “A Bioinspired Soft Robotic Gripper for

Adaptable and Effective Grasping,” Soft Robotics, vol. 2,

  • no. 3, pp. 107–116, 2015.

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 28 / 30

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References (cont.)

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

[7]

  • C. Choi, W. Schwarting, J. Delpreto, and D. Rus, “Learning

Object Grasping for Soft Robot Hands,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 2370–2377, 2018. [8]

  • J. Bohg, A. Morales, T. Asfour, D. Kragic, and S. Member,

“Data-Driven Grasp Synthesis — A Survey,” vol. 30, no. 2,

  • pp. 289–309, 2014.

[9]

  • S. Funabashi, G. Yan, A. Geier, A. Schmitz, T. Ogata, and
  • S. Sugano, “Morphology-specific convolutional neural

networks for tactile object recognition with a multi-fingered hand,” Proceedings - IEEE International Conference on Robotics and Automation, vol. 2019-May, no. 50185, pp. 57–63, 2019.

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 29 / 30

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References (cont.)

Motivation Grasping: Definition & Basics Charakteristics of Soft Robotics Grasp Synthesis Action-conditional model Conclusion References

[10] R. Calandra, A. Owens, D. Jayaraman, J. Lin, W. Yuan,

  • J. Malik, E. H. Adelson, and S. Levine, “More than a feeling:

Learning to grasp and regrasp using vision and touch,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3300–3307, 2018. [11] “Frontiers | Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition | Psychology.” [Online]. Available: https: //www.frontiersin.org/articles/10.3389/fpsyg.2017.01745/full [12] “Multi layer Perceptron (MLP) Models on Real World Banking Data.” [Online]. Available: https://becominghuman.ai/ multi-layer-perceptron-mlp-models-on-real-world-banking-data-f6dd3d7e998f

Jan-Gerrit Habekost – Bioinspired grasping in Soft Robotics 30 / 30