Dynamic movement primitives in robotics: A tutorial survey

M Saveriano, FJ Abu-Dakka… - … Journal of Robotics …, 2023 - journals.sagepub.com
Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …

Review of deep reinforcement learning-based object gras**: Techniques, open challenges, and recommendations

MQ Mohammed, KL Chung, CS Chyi - IEEE Access, 2020 - ieeexplore.ieee.org
The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

Neural dynamic policies for end-to-end sensorimotor learning

S Bahl, M Mukadam, A Gupta… - Advances in Neural …, 2020 - proceedings.neurips.cc
The current dominant paradigm in sensorimotor control, whether imitation or reinforcement
learning, is to train policies directly in raw action spaces such as torque, joint angle, or end …

Adaptive compliant skill learning for contact-rich manipulation with human in the loop

W Si, Y Guan, N Wang - IEEE Robotics and Automation Letters, 2022 - ieeexplore.ieee.org
It is essential for the robot manipulator to adapt to unexpected events and dynamic
environments while executing the physical contact-rich tasks. Although a range of methods …

Toward generalizable robotic dual-arm flip** manipulation

H Huang, C Zeng, L Cheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Robotic dual-arm manipulation often requires close cooperation between the arms. Dual-
arm manipulation tasks are always difficult to program in advance, and then, executed …

Dynamic movement primitives: Volumetric obstacle avoidance

M Ginesi, D Meli, A Calanca, D Dall'Alba… - 2019 19th …, 2019 - ieeexplore.ieee.org
Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a
demonstration. The trajectory can be learned efficiently after only one demonstration, and it …