Data Driven Inverse Kinematics of Soft Robots using Local Models
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Data Driven Inverse Kinematics of Soft Robots using Local Models. / Holsten, Fredrik; Engell-norregard, Morten Pol; Darkner, Sune; Erleben, Kenny.
2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. p. 6251-6257.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Data Driven Inverse Kinematics of Soft Robots using Local Models
AU - Holsten, Fredrik
AU - Engell-norregard, Morten Pol
AU - Darkner, Sune
AU - Erleben, Kenny
PY - 2019
Y1 - 2019
N2 - Soft robots are advantageous in terms of flexibility, safety and adaptability. It is challenging to find efficient computational approaches for planning and controlling their motion. This work takes a direct data-driven approach to learn the kinematics of the three-dimensional shape of a soft robot, by using visual markers. No prior information about the robot at hand is required. The model is oblivious to the design of the robot and type of actuation system. This allows adaptation to erroneous manufacturing. We present a highly versatile and inexpensive learning cube environment for collecting and analysing data. We prove that using multiple, lower order models of data opposed to one global, higher order model, will reduce the required data quantity, time complexity and memory complexity significantly without compromising accuracy. Further, our approach allows for embarrassingly parallelism. Yielding an overall much more simple and efficient approach.
AB - Soft robots are advantageous in terms of flexibility, safety and adaptability. It is challenging to find efficient computational approaches for planning and controlling their motion. This work takes a direct data-driven approach to learn the kinematics of the three-dimensional shape of a soft robot, by using visual markers. No prior information about the robot at hand is required. The model is oblivious to the design of the robot and type of actuation system. This allows adaptation to erroneous manufacturing. We present a highly versatile and inexpensive learning cube environment for collecting and analysing data. We prove that using multiple, lower order models of data opposed to one global, higher order model, will reduce the required data quantity, time complexity and memory complexity significantly without compromising accuracy. Further, our approach allows for embarrassingly parallelism. Yielding an overall much more simple and efficient approach.
U2 - 10.1109/ICRA.2019.8794191
DO - 10.1109/ICRA.2019.8794191
M3 - Article in proceedings
SP - 6251
EP - 6257
BT - 2019 International Conference on Robotics and Automation (ICRA)
PB - IEEE
T2 - 2019 International Conference on Robotics and Automation (ICRA)
Y2 - 20 May 2019 through 24 May 2019
ER -
ID: 228448594