Semantic Model for Robotic Arm Manipulation of Constrained Objects
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently manipulating constrained objects, such as doors and switches, due to their limited ability to understand the semantic structure and movement constraints of these objects, leading to poor situational awareness, latency, and unintuitive control.
Innovation Solution
A method involving data processing hardware that receives requests to manipulate constrained objects, generates a semantic model based on perception data, determines the optimal location and pose for a robotic arm to interact with the object, and controls the arm to perform the manipulation task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional robotic systems are used to manipulate constrained objects, then the system structure is simple, but the manipulation quality and efficiency are poor
Solution Approach 1:
The system segments the manipulation task into distinct phases: perception data acquisition, semantic model generation, interaction location determination, and robotic arm control. This segmentation allows each component to be optimized independently while improving overall manipulation efficiency of constrained objects.
Solution Approach 2:
A semantic model is introduced as an intermediary between perception data and robotic arm control. This semantic model encodes constraints and properties of the target object, enabling the system to make informed decisions about manipulation strategies without requiring complex direct control algorithms.
2Extent of automation
If automated manipulation is implemented, then human intervention is reduced, but the system requires complex semantic modeling and perception processing
Solution Approach 1:
The system performs preliminary action by generating a semantic model of the target constrained object before executing manipulation. This semantic model pre-encodes object constraints, grasp points, and movement limitations, allowing the robotic arm to automatically execute manipulation without real-time human intervention or complex runtime decision-making.
Solution Approach 2:
The robotic system serves itself by using its own perception data to generate semantic models and determine manipulation strategies autonomously. The system processes its own sensor inputs and makes independent decisions about how to manipulate constrained objects, reducing dependency on external human control.
3Manufacturing precision
If semantic modeling is used to improve grasp quality, then manipulation precision increases, but processing time and computational load increase
Solution Approach 1:
The system applies partial action by focusing semantic modeling efforts only on critical aspects of the constrained object relevant to manipulation, such as grasp points and constraint directions. Rather than modeling all object properties in full detail, the system identifies and processes only the essential features needed for successful manipulation, reducing computational overhead while maintaining grasp precision.
Data Source
AI summary
Techniques for automated constrained manipulation are provided. In one aspect, a method includes receiving a request for manipulating a target constrained object and receiving perception data from at least one sensor of a robot. The perception data indicative of the target constrained object. The method also includes receiving a semantic model of the target constrained object generated based on the perception data and determining a location for a robotic arm of the robot to interact with the target constrained object based on the semantic model and the request. The method further includes controlling the robotic arm to manipulate the target constrained object based on the location for the robotic arm to interact with the target constrained object.