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Overview: Objectives - Topics - Audience - Speakers - Endorsements - References
Animals move very differently from rigid robots, performing dynamic tasks efficiently, and interacting robustly, compliantly, and continuously with the external world through their body's elasticity. With the aim of getting closer to animals’ performance, elastic elements are purposefully introduced in the mechanical structure of soft robots. When it comes to compliant control systems, however, it turns out that achieving performance is not at all easier. This fact is intuitive for such measures of performance as positional accuracy, which is the reason industrial robots have traditionally been built for maximum rigidity.
Articulated soft robots [1,6] have rigid bodies and elastic joints with either constant or variable impedance. Their source of inspiration is the vertebrate part of the animal kingdom. Model-based control of articulated soft robots is a relatively mature research field, with both theoretical and practical results showing how soft robots can outperform classical rigid robots in various applications [2,3,4,5,7,10]. However, many problems remain unsolved, for example how to properly design feedback controllers without altering the natural softness of the robot , or how to efficiently excite the robot’s natural dynamics , just to cite a few.
In contrast, soft-bodied robots  are primarily inspired by the invertebrate world. Their structure is continuously deformable, and typically composed by innovative materials. These characteristics make the derivation of accurate and tractable dynamic models quite challenging. While the lack of tractable models prevents the direct application of classical control theory to the control of these kind of robots, it also pushes researchers to find innovative solutions to control these soft-bodied robots [11,12,13,14,16,20,23,24,25].
Despite a common origin, which is the inspiration from nature, the two fields emerged at different times and grew separately. This workshop has the objective to bring both fields together by fostering the discussion and exchange on the similarities and differences in modeling and controlling robots that have in common their inherent compliance.
Notably, in recent years great progress has been achieved in developing dynamic models approximating the behavior of continuously deformable soft robots [17,18,19,21,22]. We believe that speakers on that topic area will help define a common ground between the two worlds, allowing for a better understanding of the challenges in representing soft robot dynamics, and providing inspiration to develop new control approaches.
To conclude, the main aim of the workshop is to inspire new approaches in modeling and controlling of soft robots. We will bring together recognized experts in both modeling and control of soft robots, trying to answer questions such as: to which extent techniques and principles developed for articulated soft robots can be extended to the control of continuously deformable soft robots? In return, what methods developed for soft-bodied robots find application in the other field? What are the remaining problems that will require a specific treatment and analysis? Which are the new challenges in these fields? Selected experts will give talks on the relevant topics. A final open discussion session will review and analyze all the insights presented during the workshop, aiming at fostering discussions between different areas of expertise. The organization of a special issue in IJRR is planned to present the outcome of these discussions.
- Model-Based Control of Soft Robots
- Optimal policies in stiffness regulation
- Excitation of intrinsic resonance modes
- Augmented control linking models of rigid robots with soft robots
- FEM-based control of Soft-Bodied Robots
- Passivity-based control
- Nonlinear control
- Modeling of Soft Robots
- Minimal parameter models
- Kirchhoff Models
- Cosserat Models
- Reduced dimension Models
- Soft Contact Models
- Port-Hamiltonian systems
- Alternative Control Approaches for Soft Robots
- Learning control in Soft Robots
- Evolutionary Soft Robots
- Co-design of morphology and control
The workshop aims to attract audiences from the fields of articulated soft control, soft robotic modeling, and soft-bodied robot control. The workshop shall serve as a platform to build a common ground between these currently separated research areas. We postulate that in order to develop new control approaches for robots with inherent compliance, a stronger exchange should happen between these areas. While researchers feel fairly comfortable to propose controllers for articulated robots, the control approaches for continuously soft robots are still in their infancy. The workshop is thus open to any student and researcher working in modeling, design or control of soft robots with rigid, soft, or hybrid rigid-soft bodies.
Articulated Soft Robot Control
Alessandro De Luca, Alin Albu-Schäffer, Stefano Stramigioli, Sami Haddadin, Marco Hutter
Christian Duriez, Federico Renda, Elias Cueto
Kohei Nakajima, Nick Cheney, Cecilia Laschi
This proposed workshop is supported by IEEE RAS Technical Committee on Algorithms for Planning and Control of Robot Motion as confirmed by the Technical Committee co-chairs Sertac Karaman and Lydia Tapia. The workshop is further endorsed by the IEEE RAS Technical Committee on Robot Learning as confirmed by the Technical Committee co-chair Jen Kober and by the IEEE RAS Technical Committee on Human-Robot Interaction & Coordination as confirmed by the Technical Committee co-chair David Feil-Seifer.
1) Alin Albu-Schäffer, et al. "Soft robotics." IEEE Robotics & Automation Magazine 15.3 (2008).
2) Alin Albu-Schäffer, et al. "Dynamic modelling and control of variable stiffness actuators." Robotics and Automation (ICRA), 2010 IEEE International Conference on. IEEE, 2010.
3) Alessandro De Luca, Bruno Siciliano. "Trajectory control of a non-linear one-link flexible arm." International Journal of Control 50.5 (1989): 1699-1715.
4) Alessandro De Luca, Pasquale Lucibello. "A general algorithm for dynamic feedback linearization of robots with elastic joints." Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on. Vol. 1. IEEE, 1998.
5) Ludo C. Visser, Raffaella Carloni, Stefano Stramigioli. "Energy-efficient variable stiffness actuators." IEEE Transactions on Robotics 27.5 (2011): 865-875.
6) Stefan S. Groothuis, Stefano Stramigioli, Raffaella Carloni. "Modeling robotic manipulators powered by variable stiffness actuators: a graph-theoretic and port-hamiltonian formalism." IEEE transactions on robotics (2017): 807-818.
7) Sami Haddadin, et al. "Optimal control for maximizing link velocity of robotic variable stiffness joints." IFAC Proceedings Volumes 44.1 (2011): 6863-6871.
8) Sami Haddadin, et al. "Exploiting Elastic Energy Storage for “Blind” Cyclic Manipulation: Modeling, Stability Analysis, Control, and Experiments for Dribbling." IEEE Transactions on Robotics 34.1 (2018): 91-112.
9) Cosimo Della Santina, Mateo Bianchi, Giorgio Grioli, Franco Angelini, Manuel Catalano, Manolo Garabini, Antonio Bicchi. "Controlling soft robots: balancing feedback and feedforward elements." IEEE Robotics & Automation Magazine 24, no. 3 (2017): 75-83.
10) Cosimo Della Santina, et al. "The quest for natural machine motion: An open platform to fast-prototyping articulated soft robots." IEEE Robotics & Automation Magazine 24.1 (2017): 48-56.
11) George Thuruthel, Thomas George, Egidio Falotico, Federico Renda, Cecilia Laschi. "Learning dynamic models for open loop predictive control of soft robotic manipulators." Bioinspiration & biomimetics 12, no. 6 (2017): 066003.
12) George Thuruthel, Thomas, Yasmin Ansari, Egidio Falotico, Cecilia Laschi. "Control Strategies for Soft Robotic Manipulators: A Survey." Soft robotics (2018).
13) Kohei Nakajima. "Muscular-Hydrostat Computers: Physical Reservoir Computing for Octopus-Inspired Soft Robots." Brain Evolution by Design. Springer, Tokyo, 2017. 403-414.
14) Kohei Nakajima, et al. "Exploiting short-term memory in soft body dynamics as a computational resource." Journal of The Royal Society Interface 11.100 (2014): 20140437.
15) Daniela Rus, Mike Tolley. "Design, fabrication and control of soft robots." Nature 521.7553 (2015): 467.
16) Cosimo Della Santina, Robert K. Katzschmann, Antonio Bicchi, Daniela Rus. “Dynamic control of soft robots interacting with the environment.” International Conference on Soft Robotics (2018).
17) Federico Renda, et al. "Discrete Cosserat approach for multi-section soft robots dynamics." arXiv preprint (2017).
18) Federico Renda, et al. "Discrete Cosserat approach for soft robot dynamics: A new piece-wise constant strain model with torsion and shears." Intelligent Robots and Systems (IROS), 2016.
19) Christian Duriez, Thor Bieze. "Soft robot modeling, simulation and control in real-time." Soft Robotics: Trends, Applications and Challenges. Springer, Cham, 2017. 103-109.
20) Christian Duriez. "Control of elastic soft robots based on real-time finite element method." Robotics and Automation (ICRA), 2013.
21) Jean Chenevier, David González, J. Vicente Aguado, Francisco Chinesta, Elías Cueto. "Reduced-order modeling of soft robots." PloS one 13, no. 2 (2018): e0192052.
22) Francisco Chinesta, Pierre Ladeveze, Elías Cueto. "A short review on model order reduction based on proper generalized decomposition." Archives of Computational Methods in Engineering 18.4 (2011): 395.
23) Nick Cheney et al. "Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding." Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, 2013.
24) Nick Cheney, Josh Bongard, and Hod Lipson. "Evolving soft robots in tight spaces." Proceedings of the 2015 annual conference on Genetic and Evolutionary Computation. ACM, 2015.
25) Robert K. Katzschmann, Andrew D. Marchese, Daniela Rus. "Autonomous object manipulation using a soft planar grasping manipulator." Soft robotics 2.4 (2015): 155-164.