Robot Skill Learning for Navigation and Door Manipulation
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Solution Overview
Problem
Existing robots are limited in their ability to perform tasks in unstructured environments, such as hospitals and homes, due to challenges in perception, adaptation, and manipulation, and are often unable to execute sophisticated tasks like opening doors without pre-programming.
Innovation Solution
A robotic device equipped with sensors, a processor, and a manipulating element that can learn skills through human demonstration, exploration, and user input, allowing it to adapt and execute tasks like opening doors by generating and modifying plans based on environmental data and obstacle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots are equipped with manipulators to perform tasks in unstructured environments, then task capability is improved, but device complexity increases
Solution Approach 1:
The robot learns manipulation skills autonomously through self-exploration and interaction with objects in the environment, without requiring pre-programming or external training. The system develops its own understanding of how to manipulate objects by trial and error, sensing physical properties, and adapting its actions based on feedback from sensors and actuators.
Solution Approach 2:
The patent replaces traditional pre-programmed mechanical control systems with learning-based software systems. Instead of hardcoding manipulation sequences, the robot uses machine learning algorithms to generate manipulation plans dynamically based on environmental perception and learned physical models, substituting mechanical programming with adaptive computational processes.
2Reliability
If robots are pre-programmed to perform tasks, then task execution reliability is improved, but adaptability to unstructured environments deteriorates
Solution Approach 1:
The robot performs preliminary exploration and learning actions to build internal models of the environment and objects before executing complex tasks. It proactively gathers information about physical properties, spatial relationships, and interaction outcomes, creating a knowledge base that enables reliable task execution in novel situations without pre-programming.
Solution Approach 2:
The system continuously monitors task execution through sensors and uses feedback to adjust its actions in real-time. The feedback loop includes sensing environmental changes, evaluating task progress, and modifying manipulation strategies dynamically, enabling the robot to handle uncertainties and maintain reliability in unstructured environments.
3Adaptability or versatility
If robots continuously acquire information about the environment, then adaptability is improved, but loss of time increases
Solution Approach 1:
The robot performs preliminary exploration and mapping of the environment during periods when tasks are not being executed, building spatial models and identifying key features in advance. This proactive information gathering reduces the time needed for environmental assessment during actual task execution, as the robot can rely on pre-acquired knowledge rather than continuously sensing from scratch.
Solution Approach 2:
The system acquires information selectively rather than continuously, focusing sensing efforts on critical areas and features relevant to current and anticipated tasks. It uses partial environmental models sufficient for task execution rather than complete detailed maps, reducing information acquisition time while maintaining adequate adaptability.
4Ease of operation
If robots use pre-programmed manipulation skills, then ease of operation is improved, but adaptability to new situations deteriorates
Solution Approach 1:
The robot learns manipulation skills autonomously through self-exploration and interaction with objects in the environment, without requiring pre-programming or external training. The system develops its own understanding of how to manipulate objects by trial and error, sensing physical properties, and adapting its actions based on feedback from sensors and actuators.
Solution Approach 2:
The patent replaces traditional pre-programmed mechanical control systems with learning-based software systems. Instead of hardcoding manipulation sequences, the robot uses machine learning algorithms to generate manipulation plans dynamically based on environmental perception and learned physical models, substituting mechanical programming with adaptive computational processes.
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 execute skills and/or behaviors using cached trajectories or plans. In some embodiments, the processor can be configured to execute skills requiring navigation and manipulation behaviors.