Core Technology
Based on the foundation of proprietary technologies and a core “brain” built on a deep learning platform,
SenseTime has rapidly opened up AI application in multiple vertical scenarios.
Core Technology
  • 01Robot Simulation Platform
  • 023D Vision-Guided Robot Random Bin Picking
  • 03Vision-Driven Robot Arm Object Manipulation

01 / 03

Robot Simulation Platform

Leveraging the robot simulation platform to flexibly modify the experimental environment allows fast data collection, which helps the development and evaluation of learning-based autonomous grasping algorithms. It is implemented with modular structure so that the key module can be updated or replaced according to the requirements. The key data recorded in the simulation platform can be saved for further use.

02 / 03

3D Vision-Guided Robot Random Bin Picking

By analyzing 3D visual data, the system accurately estimates the 6Dpose of stacked objects in a complex environment. With the collision detection and motion planning algorithm, the system can guide the robot manipulator to grasp stacked object in a specified way. This technology can be applied to various industrial scenarios such as flexible object assembly, machine tending, logistic order picking, palletization and depalletizion.

03 / 03

Vision-Driven Robot Arm Object Manipulation

Deep learning and reinforcement learning methods allow the robot arm to learn autonomously. Multi-object manipulation tasks based on vision sensors (such as object manipulation/placement and parts assembly) effectively reduce hardware and system integration costs. The model can also be trained using samples in the simulation environment and then transferred to the real environment, reducing on-site debugging overheads. The technology significantly enhances the flexibility of robot use in industrial scenarios such as optimizing product assembly line in manufacturing process and upgrading multi-category object sorting system in logistics.

Core Technology
  • Robot Simulation Platform
  • 3D Vision-Guided Robot Random Bin Picking
  • Vision-Driven Robot Arm Object Manipulation

01 / 03

Robot Simulation Platform

Leveraging the robot simulation platform to flexibly modify the experimental environment allows fast data collection, which helps the development and evaluation of learning-based autonomous grasping algorithms. It is implemented with modular structure so that the key module can be updated or replaced according to the requirements. The key data recorded in the simulation platform can be saved for further use.

02 / 03

3D Vision-Guided Robot Random Bin Picking

By analyzing 3D visual data, the system accurately estimates the 6Dpose of stacked objects in a complex environment. With the collision detection and motion planning algorithm, the system can guide the robot manipulator to grasp stacked object in a specified way. This technology can be applied to various industrial scenarios such as flexible object assembly, machine tending, logistic order picking, palletization and depalletizion.

03 / 03

Vision-Driven Robot Arm Object Manipulation

Deep learning and reinforcement learning methods allow the robot arm to learn autonomously. Multi-object manipulation tasks based on vision sensors (such as object manipulation/placement and parts assembly) effectively reduce hardware and system integration costs. The model can also be trained using samples in the simulation environment and then transferred to the real environment, reducing on-site debugging overheads. The technology significantly enhances the flexibility of robot use in industrial scenarios such as optimizing product assembly line in manufacturing process and upgrading multi-category object sorting system in logistics.

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