Robot Grasp Selection Using Semantic Embedding Spaces
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Solution Overview
Problem
Current robot grasping techniques are computationally complex and resource-intensive due to large search spaces, making it challenging for robots to efficiently interact with various objects in different poses and configurations.
Innovation Solution
The use of reduced-dimensionality embeddings generated from sensor data, indexed with semantic labels, allows robots to quickly determine appropriate grasping strategies by mapping object features to embedding spaces and selecting grasp options based on nearest neighbors and pre-assigned semantic labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional grasp selection methods are used to evaluate all possible grasps for objects, then comprehensive grasp options can be identified, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent segments the grasp selection process into multiple stages: (1) generating candidate grasps using geometric primitives, (2) filtering candidates using semantic labels from embedding spaces, and (3) selecting final grasps based on task requirements. This segmentation reduces the search space at each stage, lowering computational complexity while maintaining reliability.
Solution Approach 2:
The patent performs preliminary actions by pre-computing embedding spaces and semantic labels for object categories before actual grasp selection. During runtime, the system only needs to query pre-computed semantic information rather than evaluating all possible grasps from scratch, significantly reducing real-time computational requirements.
2Reliability
If exhaustive search methods are used to determine appropriate grasps for various objects, then all possible grasp options are evaluated, but time consumption increases
Solution Approach 1:
The patent introduces embedding spaces and semantic labels as intermediaries between object perception and grasp selection. These intermediaries provide high-level semantic information (e.g., object category, functional properties) that guides the grasp selection process, allowing the system to quickly filter inappropriate grasps without exhaustive evaluation.
Solution Approach 2:
The patent changes the parameter space by transforming raw geometric grasp candidates into semantic-based selection criteria. Instead of evaluating grasps based solely on geometric fit, the system uses semantic labels (e.g., 'drinkable', 'stackable') as selection parameters, which reduces the effective search space and accelerates decision-making.
3Measurement precision
If detailed object features are processed to determine grasp strategies, then accurate grasp selection is achieved, but computational resources increase
Solution Approach 1:
The patent extracts only the most relevant semantic features from object descriptions for grasp selection, rather than processing all available object features. The embedding space methodology automatically identifies and extracts key semantic properties (e.g., shape, size, material) that are critical for grasp planning, discarding redundant information and reducing computational energy consumption.
Data Source
AI summary
Techniques described herein relate to using reduced-dimensionality embeddings generated from robot sensor data to identify predetermined semantic labels that guide robot interaction with objects. In various implementations, obtaining, from one or more sensors of a robot, sensor data that includes data indicative of an object observed in an environment in which the robot operates. The sensor data may be processed utilizing a first trained machine learning model to generate a first embedded feature vector that maps the data indicative of the object to an embedding space. Nearest neighbor(s) of the first embedded feature vector may be identified in the embedding space. Semantic label(s) may be identified based on the nearest neighbor(s). A given grasp option may be selected from enumerated grasp options previously associated with the semantic label(s). The robot may be operated to interact with the object based on the pose and using the given grasp option.