Robotic Trajectory Training Using Selective State Space Sampling
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Solution Overview
Problem
Robotic devices face challenges in efficiently navigating complex trajectories due to the need for extensive training and high variability in state parameters, leading to suboptimal performance and increased training time.
Innovation Solution
The implementation of an adaptive controller using a neuron network that employs selective state space sampling and supervised learning, allowing for focused training on difficult trajectory portions and autonomous operation by adapting learning parameters based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive training is provided to improve navigation performance on complex trajectories, then performance improves, but training time increases significantly
Solution Approach 1:
The state space is segmented into difficult and non-difficult portions based on performance metrics. The controller selectively applies training only to difficult portions of the trajectory, dividing the training problem into manageable segments that require focused attention rather than uniform training across the entire state space.
Solution Approach 2:
Different training intensities are applied to different regions of the state space. Difficult portions receive intensive supervised learning with high training input, while non-difficult portions receive minimal or no training input, allowing the controller to allocate training resources efficiently based on local performance needs.
2Adaptability or versatility
If high variability in state parameters is accommodated to improve adaptability, then versatility improves, but training complexity increases
Solution Approach 1:
The controller dynamically adjusts training parameters based on real-time performance metrics and state space characteristics. Training intensity, duration, and focus are modified adaptively as the controller learns from experience, allowing the system to handle high state parameter variability without requiring predetermined complex training schedules.
Solution Approach 2:
Performance metrics are continuously monitored and fed back to the controller to identify difficult portions of the state space. This feedback loop enables the controller to adaptively adjust training strategies, focusing computational resources on areas where performance improvement is most needed rather than uniformly training all state parameters.
Data Source
AI summary
Apparatus and methods for training and controlling of e.g., robotic devices. In one implementation, a robot may be utilized to perform a target task characterized by a target trajectory. The robot may be trained by a user using supervised learning. The user may interface to the robot, such as via a control apparatus configured to provide a teaching signal to the robot. The robot may comprise an adaptive controller comprising a neuron network, which may be configured to generate actuator control commands based on the user input and output of the learning process. During one or more learning trials, the controller may be trained to navigate a portion of the target trajectory. Individual trajectory portions may be trained during separate training trials. Some portions may be associated with robot executing complex actions and may require additional training trials and/or more dense training input compared to simpler trajectory actions.