What is Bin Picking and How to Do Bin Picking in 2024?

Bin picking is a critical task in various industries, particularly in logistics and manufacturing. It involves the process of selecting and retrieving items from a bin, which can be a container, box, or any other form of storage. Traditionally, this task has been performed manually, but advancements in technology have introduced automated solutions like the bin-picking robot. In this article, we will explore what bin picking is and why logistics needs to adopt this technology.

Understanding Bin Picking

Hand bin picking is the process of humans manually selecting objects from bins based on their tactile and visual cues. Although this approach is simple, it is time-consuming, labour-intensive, and prone to human errors. Here the robotic arm robot finds application.

Using modern technologies like computer vision, machine learning, and robotic arms, a bin-picking robot finds, locates, and pulls objects from bins. These robots can handle several objects with great accuracy and speed since they have cameras and sensors that let them “see” and evaluate their surroundings.

How to Do Bin Picking in 2024?

As we move into 2024, the field of bin picking has seen significant advancements, particularly with the integration of imitation learning and reinforcement learning (RL). These breakthrough technologies offer faster deployment, lower costs, and greater versatility, making them ideal for a variety of applications. This article explores how to achieve efficient bin picking using these modern approaches.

Imitation Learning in Bin Picking | How It Works?

The first step is Imitation learning, which involves a human operator teleoperating the bin-picking process. The algorithm then learns to replicate these actions, enabling the robotic system to perform the task autonomously.

Advantages

Speed of Deployment: A full system can be deployed within one month, and new motions or objects can be integrated in just a few days.

Low-Cost System: Unlike traditional methods, which require multiple expensive 3D cameras and structured light setups, our system utilizes two low-cost connections: a computer and an internet connection.

Applications

Imitation learning is particularly effective for complex objects, small parts, and soft objects such as clothes or paper. It is also suitable for setups requiring dexterous robotic hands.

Reinforcement Learning (RL) | How It Works?

Reinforcement learning can achieve great results by allowing the robot to learn autonomously through trial and error. It is a powerful framework for sequential decision-making problems. Reinforcement learning allows the robot to learn optimal behaviours through interaction with the environment, making it suitable for a wide range of applications like bin-picking. The robot receives rewards from the algorithm for successful actions, improving its performance over time.

Advantages

Low Cost: One reinforcement learning environment can be used for various motions, robots, and statuses, making it a cost-effective solution.

Conclusion

The integration of imitation learning and reinforcement learning in bin picking offers advantages in terms of speed, cost, and versatility. Whether dealing with complex objects, small parts, or soft materials, these technologies provide efficient and effective solutions for modern bin-picking challenges.

For more details on our solutions, please contact us or visit us at cyberus-ai.com.