Scene Graph Task and Motion Planning for Complex Robot Objects
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Solution Overview
Problem
Existing robotic control systems face challenges in automating complex task and motion planning tasks due to the need for numerous intermediate steps and limitations in handling complex objects, often requiring domain-specific files and primitive shape manipulation.
Innovation Solution
A novel method using 3D scene graphs for task and motion planning, integrating raw perception of complex objects and enabling long-horizon planning without specifying domain files, utilizing a hierarchical structure of geometric and symbolic scene graphs for task and motion planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional robotic control systems are used for task and motion planning, then automation of difficult and dangerous tasks can be achieved, but the systems require numerous intermediate steps and domain-specific files for complex objects
Solution Approach 1:
The patent introduces scene graphs as an intermediary representation layer between raw perception data and planning algorithms. Scene graphs encode spatial relationships, object properties, and task requirements in a unified structure that planning algorithms can directly process, eliminating the need for numerous intermediate processing steps while maintaining automation capability
Solution Approach 2:
The patent creates a universal scene graph representation that can handle diverse object types and task configurations without requiring domain-specific files. The scene graph structure accommodates geometric objects, symbolic relationships, and task specifications in a single framework, making the system adaptable to various complex objects and planning scenarios
2Extent of automation
If traditional robotic control systems are used for task and motion planning, then automation can be achieved, but the systems have limitations in handling complex objects and require primitive shape manipulation
Solution Approach 1:
Scene graphs serve as a mediator between perception systems and planning algorithms, encoding complex object geometries, physical properties, and spatial relationships in a structured format. This intermediary representation enables the planning system to handle diverse complex objects without requiring domain-specific adaptations
Solution Approach 2:
The patent transforms complex object representations into standardized scene graph parameters including geometric properties, symbolic labels, and spatial relationships. By changing the representation parameters from raw perception data to structured scene graph elements, the system gains versatility in handling various complex objects while maintaining automated planning capability
3Ease of manufacture
If domain-specific files are used for task and motion planning, then specific tasks can be programmed, but the programming becomes rigid and difficult to adapt to new tasks
Solution Approach 1:
The patent replaces domain-specific files with a universal scene graph representation that can encode multiple task types and object configurations. The scene graph structure allows the same planning framework to handle diverse tasks by simply changing the scene graph input, eliminating the need for separate domain files and enabling flexible adaptation to new tasks
Solution Approach 2:
The patent makes the task representation dynamic by using scene graphs that can be easily modified and updated. Rather than static domain-specific files, the scene graph structure allows flexible addition, removal, or modification of objects, relationships, and task parameters, enabling the system to adapt to new tasks without reprogramming
Data Source
AI summary
Apparatuses, systems, and techniques are described that solve task and motion planning problems. In at least one embodiment, a task and motion planning problem is modeled using a geometric scene graph that records positions and orientations of objects within a playfield, and a symbolic scene graph that represents states of objects within context of a task to be solved. In at least one embodiment, task planning is performed using symbolic scene graph, and motion planning is performed using a geometric scene graph.