Robot Skill Learning From Demonstration for Unstructured Tasks
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Solution Overview
Problem
Robots lack the ability to perform tasks in unstructured environments, such as hospitals and homes, due to the complexity of programming manipulators and adapting to dynamic conditions, limiting their functionality without pre-programmed skills.
Innovation Solution
A robotic system equipped with sensors, a processor, and a manipulating element that learns skills through human demonstrations and interactions, generating models for executing tasks in unstructured environments by identifying markers, obtaining sensory information, and adapting to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots are equipped with manipulators to perform tasks autonomously, then task automation capability is improved, but device complexity and programming difficulty increase
Solution Approach 1:
The robot learns manipulation skills autonomously through interaction with the environment and objects, without requiring pre-programming or complex configuration. The system self-adapts by observing physical interactions and generating its own control policies, thereby simplifying the overall system complexity while maintaining high automation capability
Solution Approach 2:
The patent replaces traditional mechanical control systems with learning-based software systems. Instead of programming complex manipulator movements through mechanical control logic, the system uses machine learning models that process sensory input and generate control commands, thereby reducing the complexity of the control architecture while preserving automation functionality
2Reliability
If robots are pre-programmed with manipulation skills, then task execution reliability is improved, but adaptability to unstructured environments deteriorates
Solution Approach 1:
The robot's manipulation skills are not fixed but dynamically adapted through continuous learning from environmental interactions. The system updates its policies based on real-time sensory feedback and observed outcomes, enabling it to maintain reliable task execution while adapting to novel situations and unstructured environments without requiring re-programming
Solution Approach 2:
The system implements closed-loop learning where sensory observations from physical interactions are fed back to update manipulation policies. This feedback mechanism allows the robot to learn from successes and failures, improving both task execution reliability and adaptability to new environments simultaneously by continuously refining its behavior based on actual environmental responses
3Adaptability or versatility
If robots operate in unstructured environments without pre-programmed skills, then adaptability is improved, but ability to perform manipulation tasks deteriorates
Solution Approach 1:
The system performs preliminary learning through simulated interactions and controlled physical experiments before deploying to unstructured environments. By pre-learning fundamental manipulation skills and object properties in controlled settings, the robot builds a foundation that enables effective task performance when deployed in novel unstructured environments without extensive pre-programming
Solution Approach 2:
The robot engages in continuous learning and exploration during operation, never stopping to update its manipulation policies. By continuously interacting with objects and environments, the system accumulates experience that improves both its adaptability to unstructured settings and its manipulation task performance over time, transforming the initial trade-off into a synergistic relationship
Data Source
AI summary
Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to learn an environmental constraint. In some embodiments, the processor can be configured to learn using a general model of a skill.