Robot Grasp Region Generation From 3D Point Clouds
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Solution Overview
Problem
Existing robotic systems struggle with efficient and adaptive grasping of objects in dynamic environments, lacking the ability to quickly and accurately determine optimal grasp geometries and adjust in real-time to changing conditions.
Innovation Solution
A computer-implemented method for a robot that utilizes a three-dimensional point cloud of sensor data to generate grasp regions and geometries for an end-effector, incorporating machine learning for object classification and real-time adjustment of grasp geometries based on updated sensor data to enhance grasping success.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional robotic systems are used for grasping, then the system structure is simple, but the ability to adapt to dynamic environments and determine optimal grasp geometries in real-time is poor
Solution Approach 1:
The system continuously receives sensor data about the environment and object properties, processes this feedback information through machine learning models, and adjusts grasp geometries in real-time based on the results. This closed-loop feedback mechanism enables adaptation to dynamic environments while managing complexity through intelligent processing rather than hardware redundancy.
Solution Approach 2:
The patent replaces complex mechanical adaptation mechanisms with computational approaches. Instead of using multiple physical sensors and actuators for every possible scenario, the system uses machine learning algorithms to process sensor data and determine optimal grasp geometries, substituting mechanical complexity with computational intelligence.
2Reliability
If real-time adjustment of grasp geometries is implemented, then grasping success rate improves, but computational time and processing requirements increase
Solution Approach 1:
The system pre-processes environmental data and object properties before grasping operations. Machine learning models are trained in advance to recognize object characteristics and predict optimal grasp geometries. This preliminary preparation allows for faster real-time decision-making, as the computational heavy lifting is done beforehand, reducing on-demand processing time while maintaining high success rates.
Solution Approach 2:
The system dynamically adjusts grasp geometry parameters (position, orientation, contact points) based on real-time sensor feedback. By changing these parameters adaptively rather than using fixed pre-programmed motions, the system achieves higher grasping success rates. The parameter adjustment is optimized through machine learning to minimize computational time while maximizing reliability.
3Measurement precision
If multiple sensor data sources are integrated, then measurement precision of object properties improves, but device complexity and data processing burden increase
Solution Approach 1:
The patent combines data from multiple sensor sources (depth sensors, cameras, LIDAR, tactile sensors) into a unified representation of object properties. Rather than processing each sensor stream separately, the system merges these data sources to create a comprehensive object model, achieving high measurement precision. The merging process is managed through integrated machine learning pipelines that handle multi-sensor data fusion efficiently.
Solution Approach 2:
The machine learning models are designed to process and interpret data from multiple sensor types universally. A single computational framework handles diverse sensor inputs (images, point clouds, depth maps, tactile signals), reducing the need for separate processing systems for each sensor. This multi-functional approach increases measurement precision while managing complexity through a universal processing architecture.
Data Source
AI summary
A computer-implemented method, executed by data processing hardware of a robot, includes receiving a three-dimensional point cloud of sensor data for a space within an environment about the robot. The method includes receiving a selection input indicating a user-selection of a target object represented in an image corresponding to the space. The target object is for grasping by an end-effector of a robotic manipulator of the robot. The method includes generating a grasp region for the end-effector of the robotic manipulator by projecting a plurality of rays from the selected target object of the image onto the three-dimensional point cloud of sensor data. The method includes determining a grasp geometry for the robotic manipulator to grasp the target object within the grasp region. The method includes instructing the end-effector of the robotic manipulator to grasp the target object within the grasp region based on the grasp geometry.