Probabilistic Autocomplete for Robot Programming
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Solution Overview
Problem
Current robotic teleoperation systems are slow and inefficient due to the need for fine-grained control, especially when high precision is required, leading to productivity drawbacks in tasks that can take minutes or hours to complete.
Innovation Solution
The implementation of a probabilistic autocomplete system that uses AI to learn from demonstrated tasks, allowing for the selection of affordances and actions based on augmented reality gestures or other inputs, enabling quick definition of new tasks and improving task fluency by predicting human intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine-grained control is used in robotic teleoperation, then precision is improved, but task completion time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining a library of skilled actions and affordances that can be automatically selected and executed. Instead of requiring real-time fine-grained control during task execution, the system prepares and stores action templates in advance, allowing rapid task completion through selection rather than manual control.
Solution Approach 2:
The system copies skilled actions from a library of pre-defined templates and applies them to current tasks. By copying and reusing proven action sequences from the library, the system achieves both precision (from the pre-defined skilled actions) and speed (from the reusable templates), eliminating the need for time-consuming real-time fine-grained control.
2Measurement precision
If traditional teleoperation control is used, then control accuracy is maintained, but productivity decreases
Solution Approach 1:
The system dynamically adapts the level of automation based on task complexity and context. For simple tasks, it uses automated template selection for high productivity; for complex tasks requiring human judgment, it maintains control accuracy through interactive refinement. This dynamic adaptation allows the system to optimize both productivity and accuracy across different task types.
Solution Approach 2:
The system incorporates feedback mechanisms where operators can review and correct automated action selections. This feedback loop ensures that control accuracy is maintained while leveraging automation for productivity gains, as operators can quickly correct minor deviations without manual re-execution of entire tasks.
3Productivity
If probabilistic autocomplete is implemented, then task definition speed is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task planning function into separate components: a library of pre-defined skilled actions, an affordance graph representing possible actions, and a probabilistic selection mechanism. This segmentation allows the complex system to be modular and manageable, with each component handling a specific aspect of task execution independently.
Solution Approach 2:
The affordance graph acts as an intermediary between the operator's high-level intentions and the detailed action execution. It provides a structured intermediate representation that guides the probabilistic selection process, making the system's decision-making transparent and manageable while maintaining high task definition speed.
Data Source
AI summary
Various aspects of techniques, systems, and use cases may be used for probabilistic automatic determination of an action for a robotic device. A technique may include identifying a current context of a robotic device, determining from the current context, a set of actions performable by the robotic device corresponding to at least one object, the set of actions including one or more affordances generated from a basic skills library for the robotic device, and automatically selecting an action of the set of actions based on an acyclic graph describing action paths. The technique may include outputting control signals that, when executed, cause the robotic device to perform the action to interact with the at least one object.