Robotic Skill Learning for Manipulation in Unstructured Environments
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Solution Overview
Problem
Robots lacking manipulators struggle to perform tasks in unstructured environments due to the challenge of programming and adapting to dynamic surroundings, limiting their ability to interact with objects and humans without human assistance.
Innovation Solution
A robotic apparatus equipped with sensors, a processor, and a manipulating element that learns skills through human demonstrations and environmental interactions, generating models for object manipulation and navigation based on sensory information, enabling autonomous operation in unstructured settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots are equipped with manipulators to perform tasks in unstructured environments, then the ability to interact with objects is improved, but the device complexity and programming difficulty increase significantly
Solution Approach 1:
The robot performs self-calibration and self-learning by autonomously interacting with objects in the environment. The manipulator learns object properties and manipulation skills through repeated trials and feedback, eliminating the need for complex pre-programming by operators.
Solution Approach 2:
The system continuously receives sensory feedback from sensors during manipulation tasks and uses this feedback to adjust and improve its manipulation skills. This closed-loop learning process enables the robot to adapt to unstructured environments without complex programming.
2Adaptability or versatility
If robots operate in unstructured environments without pre-programming, then the adaptability to dynamic surroundings is improved, but the reliability of task execution decreases due to uncertainty
Solution Approach 1:
The robot performs preliminary calibration by learning object properties and environmental characteristics before executing tasks. This pre-learning phase stores information about objects and constraints, enabling reliable task execution when the robot encounters similar situations.
Solution Approach 2:
The robot autonomously learns and adapts to the unstructured environment through self-directed exploration and interaction, building its own knowledge base without external programming. This self-learning capability enables reliable operation in dynamic surroundings.
3Adaptability or versatility
If robots continuously acquire information about the environment to make autonomous decisions, then the adaptability to unstructured environments is improved, but the loss of time for information processing increases
Solution Approach 1:
The robot performs preliminary learning of environmental characteristics and object properties during idle periods or before task execution. This pre-acquired knowledge is stored and reused during task execution, reducing the need for continuous information acquisition and minimizing time loss.
Solution Approach 2:
The robot acquires only the necessary portion of environmental information required for current tasks rather than continuously gathering all possible data. This selective information acquisition reduces processing time while maintaining adaptability.
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.