Robot Motion Planning Using Contact Correspondence and Neural Networks
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Solution Overview
Problem
Generating motion plans for robots is computationally intensive, requiring significant memory and time resources, especially for complex tasks and robots.
Innovation Solution
A system utilizing contact correspondence and a neural network to map demonstrated motion plans onto test objects, optimizing the mapped motion to comply with physical laws and constraints, and generating contact-rich motion plans that can be generalized across diverse objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional motion planning algorithms are used for complex robots and tasks, then motion plans can be generated, but significant computing resources including memory and time are consumed
Solution Approach 1:
The system pre-computes and stores contact correspondence data during an offline phase, creating a contact correspondence model that captures relationships between object geometries and contact points. This preliminary action eliminates the need for real-time computation of contact dynamics during motion planning, significantly reducing online computational time while maintaining planning accuracy
Solution Approach 2:
The system creates a simplified contact correspondence model that copies essential contact relationship information from complex physical simulations. This model serves as a lightweight approximation that can be quickly queried during motion planning without requiring full physics engine computations, thereby reducing computational resource consumption
2Reliability
If traditional motion planning algorithms are used for complex robots and tasks, then motion plans can be generated, but significant memory resources are consumed
Solution Approach 1:
The system extracts only the essential contact correspondence information from full physics simulations and stores it in a compact data structure. By taking out only the necessary contact point mappings and geometric relationships, the system reduces memory consumption while retaining the critical information needed for motion planning decisions
Solution Approach 2:
The contact correspondence model is pre-computed and stored in an optimized data format during an offline phase. This preliminary computation organizes contact information in a memory-efficient manner, allowing the system to avoid loading and processing large amounts of raw simulation data during online motion planning operations
3Productivity
If contact correspondence and neural networks are used to generate motion plans, then computational resources are reduced, but the system must ensure compliance with physical laws and constraints
Solution Approach 1:
The system incorporates a feedback mechanism where the contact correspondence model is trained using simulation data that enforces physical constraints. During motion planning, the model's predictions are validated against learned physical patterns, and the training process continuously refines the model to ensure compliance with physical laws while maintaining computational efficiency
Solution Approach 2:
The system transforms the motion planning problem by changing parameters from continuous physics simulations to discrete contact correspondence mappings. By representing contact relationships as pre-computed discrete states in the neural network model, the system achieves computational efficiency while the training process ensures these discrete states respect physical dynamics
Data Source
AI summary
Apparatuses, systems, and techniques to generate a motion plan. In at least one embodiment, a motion plan is generated using a neural network based, at least in part, on a demonstration of a task.