Adaptive Collision-Free Reaching Skill Learning from Demonstration

Communications in Computer and Information Science - Robotics and Rehabilitation Intelligence ◽

10.1007/978-981-33-4932-2_8 ◽

2020 ◽

pp. 105-118

Author(s):

Uchenna Emeoha Ogenyi ◽

Dalin Zhou ◽

Zhaojie Ju ◽

Honghai Liu

Keyword(s):

Skill Learning ◽

Learning From Demonstration

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Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance

2020 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra40945.2020.9197470 ◽

2020 ◽

Author(s):

M. Asif Rana ◽

Daphne Chen ◽

Jacob Williams ◽

Vivian Chu ◽

S. Reza Ahmadzadeh ◽

...

Keyword(s):

User Experience ◽

Task Complexity ◽

Skill Learning ◽

Learning From Demonstration

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Automated Robot Skill Learning from Demonstration for Various Robot Systems

KI 2019: Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-30179-8_14 ◽

2019 ◽

pp. 168-181 ◽

Cited By ~ 1

Author(s):

Lisa Gutzeit ◽

Alexander Fabisch ◽

Christoph Petzoldt ◽

Hendrik Wiese ◽

Frank Kirchner

Keyword(s):

Skill Learning ◽

Learning From Demonstration ◽

Robot Systems

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Multiform Adaptive Robot Skill Learning From Humans

Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems ◽

10.1115/dscc2017-5114 ◽

2017 ◽

Author(s):

Leidi Zhao ◽

Raheem Lawhorn ◽

Siddharth Patil ◽

Steve Susanibar ◽

Lu Lu ◽

...

Keyword(s):

Skill Acquisition ◽

Adaptive Learning ◽

Basic Element ◽

Skill Learning ◽

Human Robot Interaction ◽

Robotic Manipulation ◽

Learning From Demonstration ◽

Critical Skills ◽

Soft Objects ◽

Time Critical

Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.

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