Robot Online Training With Adaptive Trajectory Correction
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Solution Overview
Problem
Existing robotic devices face challenges in adapting to changes in their environment or model without requiring significant changes in programming code, especially when dynamics change rapidly, such as with unexpected obstacles, which can lead to inconsistent trajectory selection and increased computational resources for decision-making.
Innovation Solution
A robotic apparatus equipped with a controllable actuator, sensor module, and adaptive controller that uses a supervised learning process to select and strengthen trajectories based on sensory input and teaching signals, allowing the robot to learn and adapt by associating sensory contexts with actions through a neuron network, thereby reducing the need for constant human intervention and improving decision-making reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If programming is used to control robotic devices, then the robot can perform desired functions, but changes in robot model or environment require changes in programming code
Solution Approach 1:
The robotic device performs self-learning through exploration, automatically adapting to environmental changes without requiring external reprogramming. The system uses reinforcement learning to modify its own control policies based on observed outcomes, enabling it to handle new situations autonomously.
Solution Approach 2:
The system changes the parameters of the control policy through learning rather than modifying the underlying programming code. By adjusting policy parameters based on exploration results, the robot adapts to environmental changes while maintaining the same structural code.
2Reliability
If remote control is used to operate robotic devices, then human operators can control the robot, but user experience and agility may be inadequate when dynamics change rapidly
Solution Approach 1:
The robotic device performs preliminary exploration actions to learn about the environment and build internal models before actual tasks are executed. This advance learning enables faster and more reliable responses when dynamics change, as the system has already gathered relevant information during exploration phases.
Solution Approach 2:
The system continuously receives feedback from sensors during operation and uses this information to update its control policies in real-time. This closed-loop learning enables the robot to adapt to rapid changes in dynamics without requiring human intervention, improving both reliability and response time.
3Extent of automation
If exploration-based learning is used to train robots, then the robot can learn to operate autonomously, but computational resources are increased for decision-making
Solution Approach 1:
The system performs partial exploration rather than exhaustive search, focusing computational resources on the most promising actions and states. By using efficient exploration strategies such as intrinsic motivation and curiosity-driven learning, the robot achieves autonomous operation with reduced computational overhead compared to brute-force exploration methods.
Data Source
AI summary
Robotic devices may be trained by a user guiding the robot along a target trajectory using a correction signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. Training may comprise a plurality of trials. During an initial portion of a trial, the trainer may observe robot's operation and refrain from providing the training input to the robot. Upon observing a discrepancy between the target behavior and the actual behavior during the initial trial portion, the trainer may provide a teaching input (e.g., a correction signal) configured to affect robot's trajectory during subsequent trials. Upon completing a sufficient number of trials, the robot may be capable of navigating the trajectory in absence of the training input.