Robot Work Primitives for State Evaluation and Semi-Autonomous Control
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Solution Overview
Problem
Current robot systems require complex and elaborate tele-operation interfaces, limiting accessibility and autonomy, as they rely on sophisticated sensors and equipment that demand full pilot attention, making it difficult to evaluate state representations and control robot actions effectively.
Innovation Solution
A robot system comprising a robot body, sensors, and a controller with processor-executable instructions that access reusable work primitives and percepts to evaluate state representations, determine metric satisfaction, and transition towards goal states, enabling semi-autonomous operation and simplified control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex tele-operation interfaces with sophisticated sensors and equipment are used to control robots, then control precision and reliability are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The control system is segmented into modular components: high-level task planners that define goals, mid-level skill executors that implement specific actions, and low-level motor controllers that handle physical execution. This segmentation allows each module to be optimized independently, reducing overall system complexity while maintaining control reliability through specialized function blocks.
Solution Approach 2:
An intermediary control architecture is introduced between the operator and the robot, using state representation evaluation and metric-based decision making to bridge the gap. This intermediary layer automatically evaluates robot states, compares them against goal states, and selects appropriate actions, reducing the complexity of direct operator-robot interaction while maintaining reliable control through systematic decision processes.
2Measurement precision
If elaborate tele-operation equipment is used to ensure precise robot control, then control precision is improved, but ease of operation deteriorates due to full pilot attention requirement
Solution Approach 1:
The robot system performs self-evaluation of its state representations and self-selection of appropriate actions through metric-based decision making. The system automatically monitors its own state, compares it against goal states, and executes corrective actions without requiring continuous operator intervention, thereby maintaining precision while improving ease of operation.
Solution Approach 2:
A continuous feedback loop is established where the robot's state is constantly evaluated against metric thresholds, and action selection is adjusted based on this feedback. This automated feedback mechanism maintains measurement precision by systematically tracking state variables while improving ease of operation by eliminating the need for constant operator monitoring and adjustment.
3Measurement precision
If sophisticated sensors and equipment are deployed for robot control, then measurement precision of robot state is improved, but device complexity increases
Solution Approach 1:
The essential measurement functions are extracted from complex sensor systems and encapsulated in simplified state representation models. Rather than processing raw data from multiple sophisticated sensors, the system uses extracted state variables that capture the essential information needed for control decisions, maintaining measurement precision while reducing device complexity.
Solution Approach 2:
The system transforms complex sensor measurements into simplified parameter representations that capture the essential state information. By changing the parameter representation from raw sensor data to meaningful state variables with defined metrics, the system maintains measurement precision for control purposes while reducing the complexity of the sensor system required to achieve it.
Data Source
AI summary
Robots, systems, methods, and computer program products for completing work objectives and evaluating states of robots are described. A robot accesses a library of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-action that the robot is trained to autonomously perform. Each reusable work primitive is paired with an associated percept, which is used to evaluate a state representation of a robot to determine whether a desired outcome for the reusable work primitive is achieved.