Robotic Task Autonomy Control for Risk-Based Human Intervention
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Solution Overview
Problem
Robotic systems face challenges in achieving full autonomy due to unanticipated operational scenarios, such as varying lighting conditions and sensor failures, leading to task failures and the need for human intervention, especially in less controlled environments, where they struggle to adapt and make on-the-fly adjustments to maintain operational efficiency and safety.
Innovation Solution
The implementation of a process knowledge database that enables variable autonomy by generating contextual and semantic labels, allowing robotic systems to operate autonomously or semi-autonomously through motion planning algorithms, enabling human-robot collaboration and progressive reduction of human-controlled sub-tasks, with risk thresholds set by organizational or individual trust levels, facilitating scene-based assistance and risk balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic systems operate autonomously in unanticipated scenarios, then operational efficiency is improved, but reliability deteriorates due to unprogrammed branches like sensor failure and unrecognized objects
Solution Approach 1:
The robotic system performs self-diagnosis and self-recovery from failures using sensor data and machine learning models. When sensor failures or unanticipated scenarios occur, the system autonomously detects the issue, determines appropriate corrective actions, and executes recovery procedures without human intervention, thereby maintaining both operational efficiency and reliability
Solution Approach 2:
The system continuously monitors sensor data and operational status, using feedback loops to detect unanticipated scenarios and adjust behavior in real-time. Machine learning models analyze sensor inputs and provide feedback for adaptive decision-making, enabling the robot to handle unprogrammed branches while maintaining task completion
2Extent of automation
If full autonomy is implemented, then human intervention is reduced, but adaptability to unanticipated scenarios deteriorates due to lack of human judgment
Solution Approach 1:
The system dynamically adjusts operational parameters and autonomy levels based on scenario complexity and confidence levels of machine learning models. When encountering unanticipated scenarios, the system can modify parameters such as exploration vs. exploitation trade-offs, risk tolerance, and decision-making thresholds to adapt to new conditions while maintaining high autonomy
Solution Approach 2:
The robotic system employs dynamic task prioritization and goal reformation capabilities that allow it to adapt to unanticipated scenarios in real-time. When sensor failures or unrecognized objects are detected, the system dynamically reprioritizes tasks and reformulates goals based on current sensor data and machine learning predictions, maintaining adaptability without requiring human intervention
3Adaptability or versatility
If human directly control robotic systems, then adaptability to unanticipated scenarios is improved, but operational efficiency deteriorates due to human error and response time
Solution Approach 1:
The system introduces an intelligent intermediary layer consisting of machine learning models and decision-making algorithms that process sensor data and generate control commands. This intermediary acts as a bridge between sensors and actuators, enabling rapid adaptive responses to unanticipated scenarios while maintaining operational efficiency by eliminating human response time delays
4Object-affected harmful factors
If remote control is implemented, then human safety is improved, but device complexity increases due to bandwidth and latency constraints
Solution Approach 1:
The robotic system pre-loads machine learning models and operational parameters into local memory before remote operations begin. During remote control, the system executes pre-loaded models locally to generate control commands, only transmitting essential data and high-level instructions over the network. This preliminary action reduces bandwidth requirements and compensates for latency while maintaining human safety
Data Source
AI summary
Based on data indicative of an area proximate to a robotic device, a scene is generated. Based on information from a knowledge database, a task associated with the scene is identified. A risk threshold is determined based on the scene, the task, and one or more trust thresholds. Based on the risk threshold, a ratio of sub-tasks of the task to be controlled by a user is determined. In accordance with the risk threshold, a user input is received for controlling one or more of the sub-tasks when the ratio dictates that at least one of the sub-tasks requires user intervention.