Robotic Skill Learning From Demonstration in Unstructured Environments
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Solution Overview
Problem
Current robotic systems are limited in their ability to perform tasks in unstructured environments, such as hospitals and homes, due to the challenge of programming manipulators and adapting to dynamic changes, as they require complete knowledge of their surroundings and cannot rely on pre-programmed manipulation skills.
Innovation Solution
A robotic system equipped with sensors, a processor, and a manipulating element that can learn and execute skills through human demonstrations and interactions, using machine-learning techniques to adapt to unstructured environments and perform tasks without pre-programmed manipulation skills, by identifying markers, generating models for movement, and interacting with physical objects and humans.
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 programming complexity and device complexity increase
Solution Approach 1:
The robotic system performs self-learning through demonstration, automatically generating manipulation skills without requiring external programming. The system observes human demonstrations, processes the sensory information, and autonomously creates models for execution, eliminating the need for complex pre-programming while achieving autonomous task automation
Solution Approach 2:
The system performs preliminary learning through human demonstrations before actual task execution. By capturing and processing demonstration data in advance, the robotic system builds reusable skill models that can be executed later without requiring complex real-time programming decisions
2Adaptability or versatility
If robots operate in unstructured environments, then adaptability is improved, but ability to perform tasks deteriorates due to lack of complete environmental knowledge
Solution Approach 1:
The system continuously updates its environmental model by comparing sensor observations with predicted observations from its skill models. This feedback mechanism allows the robotic system to adapt to unstructured environments while maintaining reliable task execution through continuous verification and adjustment of its understanding
Solution Approach 2:
The robotic system employs dynamic skill models that can be adjusted and refined based on observed environmental conditions. The system transitions from static pre-programmed behaviors to dynamic, adaptive behaviors that respond to changing environmental conditions while maintaining task completion reliability
3Reliability
If robots use pre-programmed manipulation skills, then task execution reliability is improved, but adaptability to new environments deteriorates
Solution Approach 1:
The system copies human manipulation behaviors through observation and demonstration. By replicating human skills rather than using rigid pre-programmed sequences, the robotic system achieves both reliability (through consistent skill execution) and adaptability (through the ability to learn new skills from demonstrations in different environments)
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.