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Google has developed a garbage sorting robot that can remove recyclables and compost from office waste

The  robots trained with Reinforcement Learning (RL) almost always sort the garbage correctly

google robots

Google has developed garbage-separating robots that can remove recyclables and compost from office waste. As a result, residual waste was significantly reduced.

Mountain View (United States) . A team at Google has been working on garbage-sorting robots  for about two years , according to the research paper Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators (PDF) . The 23 robots were trained using reinforcement learning (RL) technology. The scientists used both real data and a simulation as training data.

The task for the robots was to patrol within an office complex and inspect waste separation stations, which contain containers for recyclable materials, compost and residual waste. Their objective was to classify recyclable objects such as beverage cans and bottles in the recycling bin, compostable waste in the organic waste bin and all other materials in the residual waste bin.

Garbage from different objects

The main problem was training the robots to identify a large number of different objects and to classify them appropriately in the respective waste bins. To develop this, Google's engineers a four-phase system to optimize the robot's ability to sort waste correctly. In the first phase, basic guidelines for waste separation were created in order to give the robots an initial wealth of experience.

However, this basis proved insufficient. Therefore, in the second phase, the system was trained using a simulation. In the third phase, the robots then went through a learning process using reinforcement learning at a garbage station with representative waste objects in order to learn how to sort them correctly. In the fourth and final phase, the robots were used at real waste stations to consolidate the waste separation skills they had acquired.

Garbage almost always sorted correctly

In the course of the training process, the system completed a total of 540,000 trials at the training waste stations and 32,500 trials at real waste stations. The overall performance of the system continued to improve as the amount of data increased. Engineers evaluated the trained system's performance at a garbage station under controlled conditions, correctly sorting 84 percent of objects.

Furthermore, the Google engineers collected statistical data from three robot deployments in the period from 2021 to 2022. Analysis of the results showed that the weight of the residual waste could be reduced by around 40 to 50 percent. The experiments carried out illustrated that a stepped reinforcement learning strategy can be promising. Nevertheless, the possibilities here have not yet been fully exhausted. In the future, the Google engineers would like to integrate other sources of information, such as learning from Internet videos, into the training proce

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