Robot learning by demonstration
نویسندگان
چکیده
منابع مشابه
Robot Learning by Demonstration
In this report, two systems have been developed for robot behavior acquisition using kinesthetic demonstrations. The first enables a humanoid robot to imitate constrained reaching gestures directed towards a target using a learning algorithm based on Gaussian Mixture Regression. The imitation trajectory can be reshaped in order to satisfy the constraints of the task and it can adapt to changes ...
متن کاملMulti Robot Learning by Demonstration
In this paper, we investigate the feasibility of a Multi Robot Learning by Demonstration system, which allows multiple teachers to give a demonstration to multiple robots simultaneously. A novel, complete end-to-end system was developed, which extracts data from a live human group demonstration, and allows the robots to imitate the demonstration by adapting the demonstration dataset to the curr...
متن کاملRobot learning from demonstration
1. Motivation Programming by demonstration (PbD) is a key research topic in robotics. It impacts both fundamental research and application-oriented studies. Work in that area tackles the development of robust algorithms for motor control, motor learning, gesture recognition and visuo-motor integration. While the field existed for more than 20 years, recent developments, taking inspiration in bi...
متن کاملRobot Learning From Demonstration
The goal of robot learning from demonstration is to have a robot learn from watching a demonstration of the task to be performed. In our approach to learning from demonstration the robot learns a reward function from the demonstration and a task model from repeated attempts to perform the task. A policy is computed based on the learned reward function and task model. Lessons learned from an imp...
متن کاملLearning By Demonstration for a Humanoid Robot Using Clustering
Robot skill acquisition can be conceptualized as a task of identifying patterns in a spatio-temporal sensory feature space. Skills can be mathematically learned through identifying mappings from sensory signals into Qualitative States (QS), construction of a QS skill automaton, and the detection of motor or output commands that transition the model from the present QS to the next QS. In this pa...
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ژورنال
عنوان ژورنال: Scholarpedia
سال: 2013
ISSN: 1941-6016
DOI: 10.4249/scholarpedia.3824