scholarly journals A Non-Linear Manifold Alignment Approach to Robot Learning from Demonstrations

2018 ◽  
Vol 30 (2) ◽  
pp. 265-281 ◽  
Author(s):  
Ndivhuwo Makondo ◽  
Michihisa Hiratsuka ◽  
Benjamin Rosman ◽  
Osamu Hasegawa ◽  
◽  
...  
Keyword(s):  
Linear Manifold ◽  
Robot Learning ◽  
Procrustes Analysis ◽  
Human Model ◽  
Dynamic Movement ◽  
Learning From Demonstrations ◽  
Manifold Alignment ◽  
Alignment Technique ◽  
Dynamic Movement Primitives ◽  
Skeletal Model

The number and variety of robots active in real-world environments are growing, as well as the skills they are expected to acquire, and to this end we present an approach for non-robotics-expert users to be able to easily teach a skill to a robot with potentially different, but unknown, kinematics from humans. This paper proposes a method that enables robots with unknown kinematics to learn skills from demonstrations. Our proposed method requires a motion trajectory obtained from human demonstrations via a vision-based system, which is then projected onto a corresponding human skeletal model. The kinematics mapping between the robot and the human model is learned by employing Local Procrustes Analysis, a manifold alignment technique which enables the transfer of the demonstrated trajectory from the human model to the robot. Finally, the transferred trajectory is encoded onto a parameterized motion skill, using Dynamic Movement Primitives, allowing it to be generalized to different situations. Experiments in simulation on the PR2 and Meka robots show that our method is able to correctly imitate various skills demonstrated by a human, and an analysis of the transfer of the acquired skills between the two robots is provided.

scholarly journals An Extended DMPs framework for Decoupled Quaternions Learning and generalization

2021 ◽  
Author(s):  
Zhiwei Liao ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Xuesong Mei
Keyword(s):  
Riemannian Manifolds ◽  
Gaussian Mixture ◽  
Joint Space ◽  
Robot Learning ◽  
Learning From Demonstration ◽  
End Effector ◽  
Mixture Regression ◽  
Cartesian Space ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

Abstract Dynamic Movement Primitives (DMPs) as a robust and efficient framework has been studied widely for robot learning from demonstration. Classical DMPs framework mainly focuses on the movement learning in Cartesian or joint space, and can't properly represent end-effector orientation. In this paper, we present an Extended DMPs framework (EDMPs) both in Cartesian space and Riemannian manifolds for Quaternion-based orientations learning and generalization. Gaussian Mixture Model and Gaussian Mixture Regression are adopted as the initialization phase of EDMPs to handle multi-demonstrations and obtain their mean and covariance. Additionally, some evaluation indicators including reachability and similarity are defined to characterize the learning and generalization abilities of EDMPs. Finally, the quaternion-based orientations are successfully transferred from human to the robot, and a real-world experiment is conducted to verify the effectiveness of the proposed method. The experimental results reveal that the presented approach can learn and generalize multi-space parameters under multi-demonstrations.

scholarly journals The Generalization of Robot Skills Based on Dynamic Movement Primitives

2020 ◽  
Vol 53 (5) ◽  
pp. 265-270
Author(s):  
Xian Li ◽  
Chenguang Yang ◽  
Ying Feng
Keyword(s):  
Movement Primitives ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

Learning from demonstration using improved dynamic movement primitives

2021 ◽  
Author(s):  
Tiantian Wang ◽  
Liang Yan ◽  
Gang Wang ◽  
Xiaoshan Gao ◽  
Nannan Du ◽  
...  
Keyword(s):  
Learning From Demonstration ◽  
Movement Primitives ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

scholarly journals An Adaptive Imitation Learning Framework for Robotic Complex Contact-Rich Insertion Tasks

2022 ◽  
Vol 8 ◽  
Author(s):  
Yan Wang ◽  
Cristian C. Beltran-Hernandez ◽  
Weiwei Wan ◽  
Kensuke Harada
Keyword(s):  
Force Control ◽  
Robotic Manipulation ◽  
Imitation Learning ◽  
Specific Class ◽  
High Quality ◽  
Single Task ◽  
Learning Framework ◽  
Dynamic Movement ◽  
Real Hardware ◽  
Dynamic Movement Primitives

Complex contact-rich insertion is a ubiquitous robotic manipulation skill and usually involves nonlinear and low-clearance insertion trajectories as well as varying force requirements. A hybrid trajectory and force learning framework can be utilized to generate high-quality trajectories by imitation learning and find suitable force control policies efficiently by reinforcement learning. However, with the mentioned approach, many human demonstrations are necessary to learn several tasks even when those tasks require topologically similar trajectories. Therefore, to reduce human repetitive teaching efforts for new tasks, we present an adaptive imitation framework for robot manipulation. The main contribution of this work is the development of a framework that introduces dynamic movement primitives into a hybrid trajectory and force learning framework to learn a specific class of complex contact-rich insertion tasks based on the trajectory profile of a single task instance belonging to the task class. Through experimental evaluations, we validate that the proposed framework is sample efficient, safer, and generalizes better at learning complex contact-rich insertion tasks on both simulation environments and on real hardware.

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