Robotic Skill Learning With Behavior Arbitration for Dynamic Tasks
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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 conditions, as they cannot rely on pre-programmed knowledge of their surroundings.
Innovation Solution
A robotic system equipped with sensors, a processor, and a manipulating element that can learn skills through human demonstrations and interactions, allowing it to perceive and adapt to its environment by generating models for executing physical interactions with objects, including humans, and using behavior arbitration to act autonomously in dynamic settings.
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 through self-supervised learning from sensor data collected during autonomous exploration and human demonstrations, eliminating the need for complex pre-programming of manipulator operations
Solution Approach 2:
Traditional mechanical control programming is replaced with machine learning models that process sensor inputs and generate manipulator control commands, substituting complex programming with data-driven adaptive control
2Reliability
If robots are designed to operate in structured environments with pre-programmed knowledge, then operational reliability is improved, but adaptability to unstructured environments deteriorates
Solution Approach 1:
The robot transitions from static pre-programmed knowledge to dynamic learning, continuously adapting its manipulation skills based on real-time sensor feedback and changing environmental conditions in unstructured settings
Solution Approach 2:
The robot uses sensor data from autonomous exploration and human demonstrations as feedback to continuously learn and refine manipulation skills, enabling reliable operation in previously unpredictable unstructured environments
3Adaptability or versatility
If robots continuously acquire information about the environment to make autonomous decisions, then adaptability to dynamic environments is improved, but device complexity and computational requirements increase
Solution Approach 1:
The robot performs preliminary autonomous exploration to collect sensor data about the environment before executing specific tasks, pre-acquiring information that reduces computational complexity during actual task execution
Solution Approach 2:
The robot creates internal models of the environment and objects based on sensor data from exploration and demonstrations, using these copied representations to make decisions without continuously processing raw sensor inputs
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 is 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 is configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor is configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor is configured to learn an environmental constraint. In some embodiments, the processor is configured to learn using a general model of a skill.