Robotic Task Execution via Dynamic Sub-Goal Controller Selection
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Solution Overview
Problem
Existing robotic control systems face challenges in efficiently accomplishing task-level goals, particularly in dynamic environments, as they require complex choreographed operations that can be difficult to compute and may fail due to changes in the robot's state or environment.
Innovation Solution
The system decomposes task-level goals into sub-goals and maps each sub-goal to specific conditions, using a controller selection technique that evaluates priority controllers to adapt to changing states and environments, allowing the robot to select appropriate locomotion and manipulation controllers based on system parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex choreographed operations are used to accomplish task-level goals, then the robot can perform precise tasks, but the system becomes difficult to compute and may fail due to state changes
Solution Approach 1:
The patent segments complex task-level goals into multiple hierarchical sub-goals. Each sub-goal represents a simpler, more manageable component of the overall task. The control system processes sub-goals individually rather than computing entire choreographed operation sequences, reducing computational complexity while maintaining task execution precision through systematic decomposition of control objectives.
Solution Approach 2:
The patent implements dynamic sub-goal selection based on current system state parameters. The robot evaluates multiple candidate sub-goals and selects the most appropriate one based on real-time conditions, allowing the control system to adapt to state changes without requiring complete re-computation of choreographed sequences. This dynamic approach maintains precision while reducing computational burden.
2Ease of operation
If fixed control sequences are used, then task execution is straightforward, but the system fails when robot state or environment changes
Solution Approach 1:
The patent employs dynamic sub-goal selection where the control system continuously evaluates current system state parameters and selects appropriate sub-goals based on real-time conditions. This allows the robot to adapt to environmental changes and state variations while maintaining relatively simple control logic, as the system only needs to select from predefined sub-goals rather than compute entirely new sequences.
Solution Approach 2:
The patent implements feedback mechanisms where system state parameters are continuously monitored and used to influence sub-goal selection. The control system receives feedback about current robot state and environmental conditions, then adjusts sub-goal selection accordingly, enabling adaptation to changes while maintaining operational simplicity through rule-based selection criteria.
3Adaptability or versatility
If multiple controllers are available for selection, then the robot can adapt to different conditions, but the controller selection process becomes more complex
Solution Approach 1:
The patent segments the controller selection problem into hierarchical levels: task-level goals are decomposed into sub-goals, and each sub-goal is associated with specific candidate controllers. This segmentation allows the system to manage multiple controllers without overwhelming complexity, as controller selection is localized to specific sub-goal contexts rather than requiring global controller selection for entire task sequences.
Solution Approach 2:
The patent implements dynamic controller selection based on current system state parameters. Rather than statically assigning controllers to tasks, the system evaluates multiple candidate controllers for each sub-goal and selects the most appropriate one based on real-time conditions. This dynamic approach enables adaptability while managing complexity through context-specific selection rather than comprehensive controller management.
Data Source
AI summary
The present disclosure relates to methods and systems for robust robotic task execution. An example method includes obtaining a task-level goal for a robot associated with one or more sub-goals, where accomplishment of the one or more sub-goals accomplishes the task-level goal. Carrying out an operation in pursuance of a given sub-goal may involve controlling at least one actuator of the robot. The method also includes determining one or more parameters indicative of a state of a system that includes the robot and an environment proximate to the robot. The method further includes selecting a particular sub-goal based on at least one of the one or more parameters. Additionally, the method includes selecting at least one controller based on at least one of the one or more parameters and the selected sub-goal. Further, the method includes causing the robot to operate in accordance with the at least one selected controller.