Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A critical challenge in robot learning from demonstration is the ability to map the behavior of the trainer onto the robot?s existing repertoire of basic/primitive capabilities. Following a behavior-based approach,  aim to express a teacher?s demonstration as a linear combination (or fusion) of the robot?s primitives.  treat this problem as a state estimation problem over the space of possible linear fusion ights.  consider this fusion state to be a model of the teacher?s control policy expressed with respect to the robot?s capabilities. Once estimated under various sensory preconditions, fusion state estimates are used as a coordination policy for online robot control to imitate the teacher?s decision making. A particle filter is used to infer fusion state from control commands demonstrated by the teacher and predicted by each primitive. The particle filter allows for inference under the ambiguity over a large space of likely fusion combinations and dynamic changes to the teacher?s policy over time.  present results of our approach in a simulated and real world environments with a Pioneer 3DX mobile robot.
['Monica N. Nicolescu', 'Odest Chadwicke Jenkins', 'Adam Olenderski']
I am writing a research paper on a idea Learning Behavior Fusion Estimation from Demonstration
[{"from": "human", "value": "I am writing a research paper on a idea Learning Behavior Fusion Estimation from Demonstration"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A critical challenge in robot learning from demonstration is the ability to map the behavior of the trainer onto the robot?s existing repertoire of basic/primitive capabilities. Following a behavior-based approach,  aim to express a teacher?s demonstration as a linear combination (or fusion) of the robot?s primitives.  treat this problem as a state estimation problem over the space of possible linear fusion ights.  consider this fusion state to be a model of the teacher?s control policy expressed with respect to the robot?s capabilities. Once estimated under various sensory preconditions, fusion state estimates are used as a coordination policy for online robot control to imitate the teacher?s decision making. A particle filter is used to infer fusion state from control commands demonstrated by the teacher and predicted by each primitive. The particle filter allows for inference under the ambiguity over a large space of likely fusion combinations and dynamic changes to the teacher?s policy over time.  present results of our approach in a simulated and real world environments with a Pioneer 3DX mobile robot."}]