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Learning-from-Observation 2.0 : Automatic Acquisition of Robot Behavior from Human Demonstration / by Katsushi Ikeuchi, Naoki Wake, Jun Takamatsu, Kazuhiro Sasabuchi.
Springer Nature - Synthesis Collection of Technology (R0) eBook Collection 2026 Available online
View online- Format:
- Book
- Author/Creator:
- Ikeuchi, Katsushi.
- Series:
- Synthesis Lectures on Computer Vision, 2153-1064
- Language:
- English
- Subjects (All):
- Computer vision.
- Image processing--Digital techniques.
- Image processing.
- Robotics.
- Computer science.
- Computers.
- Computer Vision.
- Computer Imaging, Vision, Pattern Recognition and Graphics.
- Robotic Engineering.
- Computer Science.
- Computer Hardware.
- Local Subjects:
- Computer Vision.
- Computer Imaging, Vision, Pattern Recognition and Graphics.
- Robotics.
- Robotic Engineering.
- Computer Science.
- Computer Hardware.
- Physical Description:
- 1 online resource (218 pages)
- Edition:
- 1st ed. 2026.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2026.
- Summary:
- This book presents recent breakthroughs in the field of Learning-from-Observation (LfO) resulting from advancement in large language models (LLM) and reinforcement learning (RL) and positions it in the context of historical developments in the area. LfO involves observing human behaviors and generating robot actions that mimic these behaviors. While LfO may appear similar, on the surface, to Imitation Learning (IL) in the machine learning community and Programing-by-Demonstration (PbD) in the robotics community, a significant difference lies in the fact that these methods directly imitate human hand movements, whereas LfO encodes human behaviors into the abstract representations and then maps these representations onto the currently available hardware (individual body) of the robot, thus indirectly mimicking them. This indirect imitation allows for absorbing changes in the surrounding environment and differences in robot hardware. Additionally, by passing through this abstract representation, filtering can occur, distinguishing between important and less important aspects of human behavior, enabling imitation with fewer demonstrations and less demanding demonstrations. The authors have been researching the LfO paradigm for the past decade or so. Previously, the focus was primarily on designing necessary and sufficient task representations to define specific task domains such as assembly of machine parts, knot-tying, and human dance movements. Recent advancements in Generative Pre-trained Transformers (GPT) and RL have led to groundbreaking developments in methods to obtain and map these abstract representations. By utilizing GPT, the authors can automatically generate abstract representations from videos, and by employing RL-trained agent libraries, implementing robot actions becomes more feasible. In addition, this book: Provides explanations of task encoders utilizing GPT and agent libraries via RL for executable programs for robots Examines the selection and design of agent libraries that satisfy necessary and sufficient conditions for task domains Discusses LfO with Piaget's child development theory and offers a historical retrospective of LfO research.
- Contents:
- Encoder and its Operation
- Decoder and its Operation
- Grasp-skill Library and its Design
- Mainpulation-skill Library and its Design
- Big Bang of LfO
- Poloyhedral World
- Knot Tying World
- Dance World.
- Notes:
- Description based on publisher supplied metadata and other sources.
- Other Format:
- Print version: Ikeuchi, Katsushi Learning-From-Observation 2. 0
- ISBN:
- 9783032034458
- OCLC:
- 1549519060
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