Robot Space Extrapolation for High-Level Object Manipulation
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Solution Overview
Problem
Existing robot control systems are inefficient for general-purpose robots in unpredictable environments, requiring detailed instructions for tasks like cleaning cluttered spaces, as they lack the ability to understand abstract commands effectively.
Innovation Solution
A method that allows users to provide rough approximations of spaces using gestures and voice commands, which are then extrapolated into defined areas for the robot to manipulate objects within those spaces, such as picking up or rearranging items, using vision sensors, edge detection, and 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed instructions are provided for each object manipulation, then the robot can accurately perform tasks, but the operator efficiency decreases and the process becomes time-consuming
Solution Approach 1:
The system segments the instruction process into two levels: high-level spatial commands from the operator and low-level individual object manipulation commands generated automatically by the system. The operator only needs to specify the target space and action type, while the system automatically identifies and sequences individual object operations within that space.
Solution Approach 2:
The robot system performs self-service by automatically generating detailed manipulation instructions for individual objects based on the operator's high-level spatial command. The system independently identifies objects within the specified space, determines their properties, and sequences the manipulation operations without requiring operator intervention for each object.
2Productivity
If abstract commands are used for robot control, then operator efficiency improves, but the system cannot effectively handle unpredictable environments with multiple heterogeneous objects
Solution Approach 1:
The system introduces a spatial dimension to command abstraction by allowing operators to specify three-dimensional spaces rather than individual objects. This dimensional shift enables a single command to encompass multiple objects and operations, dramatically improving operator efficiency while maintaining adaptability through automatic object identification within the specified space.
Solution Approach 2:
The system creates a universal command structure that can handle diverse object types and manipulation tasks through a single high-level interface. The same spatial command framework works for different object categories (toys, dishes, tools), different actions (pick up, clean, arrange), and different environment types, making the system both efficient and adaptable.
3Reliability
If the robot is instructed to manipulate every object individually, then complete task execution is achieved, but the complexity of control increases significantly
Solution Approach 1:
The system merges multiple individual object identification and command generation steps into a single spatial operation. Instead of requiring separate commands for each object, the operator issues one command targeting a three-dimensional space, and the system combines object detection, classification, and operation sequencing into an automated multi-function process.
Solution Approach 2:
The spatial parameter acts as an intermediary between the operator's intent and the individual object manipulations. Rather than directly commanding each object, the operator specifies the spatial region, and the system uses this spatial intermediary to automatically identify objects and generate appropriate manipulation sequences, reducing control complexity while ensuring completeness.
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for enabling users to approximately identify a space within an environment inhabited by a plurality of objects that user wishes for a robot to manipulate. In various implementations, an approximation of a space within an environment may be identified based on user input. The actual space within the environment may then be extrapolated based at least in part on the approximation and one or more attributes of the environment. A plurality of objects that are co-present within the space and that are to be manipulated by a robot may be identified. The robot may then be operated to manipulate the identified plurality of objects.