Robotic Skill Learning With Sensor Feedback for Unstructured Tasks
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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 the challenge of programming manipulators and adapting to dynamic conditions.
Innovation Solution
A robotic device equipped with a base, a manipulating element with an end effector, sensors, and a processor that can learn and execute skills through human demonstrations, exploration, and interactions, allowing it to adapt to unstructured environments and perform sophisticated tasks like opening doors.
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 and programming difficulty increase
Solution Approach 1:
The robot performs self-calibration and self-learning through autonomous exploration and interaction with the environment. The system automatically adjusts its manipulator parameters and learns task execution through trial-and-error, eliminating the need for complex pre-programming and manual calibration procedures.
Solution Approach 2:
The robot uses sensor feedback from cameras, force sensors, and other perception systems to continuously monitor its manipulator's position and interaction forces. This feedback loop enables real-time adjustment of manipulation parameters and facilitates learning from environmental responses, reducing programming complexity while maintaining task capability.
2Reliability
If robots are pre-programmed to perform tasks in structured environments, then task execution reliability is improved, but adaptability to unstructured environments deteriorates
Solution Approach 1:
The robot performs preliminary exploration and mapping of the unstructured environment before executing tasks. It预先 identifies key features, obstacles, and interaction points through autonomous navigation and sensing, creating a preliminary model that enables reliable task execution without pre-programming for specific environmental configurations.
Solution Approach 2:
The robot employs dynamic task planning and adaptive control that allows real-time modification of task execution parameters based on environmental conditions. The system can dynamically adjust navigation paths, manipulator trajectories, and interaction forces to maintain reliable task completion in changing unstructured environments.
3Adaptability or versatility
If robots continuously acquire information about the environment to make autonomous decisions, then adaptability is improved, but information processing complexity and computational requirements increase
Solution Approach 1:
The robot extracts and focuses on only the most relevant environmental information needed for current tasks, rather than processing all available sensor data. The system selectively attends to key features such as door handles, buttons, and obstacles while filtering out irrelevant information, reducing computational complexity while maintaining adaptability.
Solution Approach 2:
The robot divides environmental perception into separate modular processing streams for different task types (navigation, manipulation, human interaction). Each stream processes specific types of information independently, allowing parallel processing and reducing overall computational complexity while maintaining comprehensive environmental awareness.
4Device complexity
If robots are designed to perform simple tasks in structured environments, then device simplicity is improved, but task sophistication and versatility deteriorate
Solution Approach 1:
The robot employs a universal manipulator design with standardized end-effectors and control architecture that can perform multiple different tasks through software configuration rather than hardware specialization. This allows simple physical hardware to achieve sophisticated task capabilities across diverse applications including door manipulation, object handling, and human interaction.
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.