TY - GEN
T1 - Towards Reversible Dynamic Movement Primitives
AU - Juan, Iñigo Iturrate San
AU - Sloth, Christoffer
AU - Kramberger, Aljaz
AU - Petersen, Henrik Gordon
AU - Østergård, Esben Hallundbæk
AU - Savarimuthu, Thiusius Rajeeth
PY - 2019
Y1 - 2019
N2 - In this paper we present an initial approach towards reversible robot movement primitives. Our approach is a modification of Dynamic Movement Primitives (DMPs), a widely used framework for robot learning from demonstration. DMPs are based on dynamical systems to guarantee properties such as convergence to a goal state, robustness to perturbation, and the ability to generalize to other goal states. Yet a main limitation of their original formulation is that they do not allow for movements to be reversed. Thus, to execute the same task forwards and backwards would mean to learn two separate primitives. We propose to replace the transformation system in DMPs with the Logistic Differential Equation (LDE), a known time-reversible non-linear system. Similarly to the original DMP formulation, our system's temporal evolution is controlled by a phase system, which in our case is derived from the LDE to guarantee reversibility. We evaluate our approach experimentally with demonstration data from a real robot assembly task, and show comparable properties to those of the original DMP system.
AB - In this paper we present an initial approach towards reversible robot movement primitives. Our approach is a modification of Dynamic Movement Primitives (DMPs), a widely used framework for robot learning from demonstration. DMPs are based on dynamical systems to guarantee properties such as convergence to a goal state, robustness to perturbation, and the ability to generalize to other goal states. Yet a main limitation of their original formulation is that they do not allow for movements to be reversed. Thus, to execute the same task forwards and backwards would mean to learn two separate primitives. We propose to replace the transformation system in DMPs with the Logistic Differential Equation (LDE), a known time-reversible non-linear system. Similarly to the original DMP formulation, our system's temporal evolution is controlled by a phase system, which in our case is derived from the LDE to guarantee reversibility. We evaluate our approach experimentally with demonstration data from a real robot assembly task, and show comparable properties to those of the original DMP system.
U2 - 10.1109/iros40897.2019.8968270
DO - 10.1109/iros40897.2019.8968270
M3 - Article in proceedings
SN - 978-1-7281-4005-6
T3 - I E E E International Conference on Intelligent Robots and Systems. Proceedings
SP - 5063
EP - 5070
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PB - IEEE
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Y2 - 3 November 2019 through 8 November 2019
ER -