We developed AutoGrasp™, an AI grasping technology based on techniques in deep learning and reinforcement learning. Our proprietary AI model continuously learns and improves with our robots - generating a variety of skills that enable operation in the real world, such as the fast-evolving landscape of e-commerce retail.

 

4 Core Elements to AutoGrasp™

 
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1. Vision

Our robot is able to see and distinguish individual items in unstructured environments typical of commercial operations. Once items are identified, AutoGrasp™ will identify which item has the highest confidence score for a successful pick. 

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2. Motion

Once an item is identified, AutoGrasp™ creates a motion plan to map out the best path for the robotic arm to approach the item, including obstacle avoidance.

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3. Action

Once the robotic arm has successfully navigated to the desired item, AutoGrasp™ will calculate the best gripper position needed, select the appropriate force required to grasp the item, and verify successful action completion. 

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4. Placement

With the single item picked,  AutoGrasp™ moves the robotic arm to the correct bin location based on information received from the WMS provider.

 
 

Our Data Model

 
 
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Robot Pilot Center

If AutoGrasp™ reaches a minimum confidence threshold when attempting a pick, our human robot pilots can remote in and assist. The benefits of our human-in-the-loop approach are twofold: 1. Ensures we deliver a consistent and reliable service to our customers 2. Each human-assisted pick provides valuable data that is fed back into our algorithms.

 
 
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Real-Time
Production Data

Operating in commercial production environments provides a continuous collection of data from our robots comprehensive sensor suite.

 
 

RESEARCH

Read the latest in Kindred's AI research.