Robots are more effective than humans in the recycling industry


Automated robotic vacuum cleaners have removed dust and debris from homes for years. Embedding AI into these technologies is becoming the standard because it expands their abilities and sharpens accuracy.

Recycling facilities understand adopting robots helps the planet, supports manual workers, and makes the sector more cost-effective and attractive. There is proof robots are powerful waste sorters. The data is an optimistic insight into the future of circular economies and empowered recycling workforces.

How Are Robots Integrating Into Recycling Facilities?

Blue-collar workers in material recovery facilities (MRFs) worry managers will replace their jobs with AI-powered robotics. The likelihood is slim in this field because there are so many vacancies, as employee shortages permeate most industrial settings. It becomes more commonplace yearly that robots of comparable size to humans work safely alongside manual labor.

For example, pick-and-place robots are a go-to installation for MRFs. They work well in tandem with other machines and have high customization options and exactness. Pair sorting robots with autonomous vehicles and delivery bots, and workforces have more room in their schedule for high-value tasks.

The machinery is programmable to target specific materials, sorting with increased reliability over time despite cosmetic variations or orientation. Research and development could make them reach 100% accuracy.

The public doesn’t view sorting garbage as glamorous, but it’s vital for green societies. It’s easier to implement waste-sorting robotics than it is to shift cultural mindsets, making these roles more alluring.

How Do They Compare to Human Sorters?

Staffing percentages vary across MRFs. Some places only fill 20% of positions, while others have 80%. Consider the effectiveness of manual sorters versus AI and optical robots. Humans can pick at 50 to 80 per hour (PPH). AI makes this a consistent 80 PPH, while optical robotics catapults this number to 1,000 PPH.

Robotics are engineered for these roles because intrinsic motivation doesn’t detract from performance. Fatigue, disgust, and illness are only a few factors reducing manual performance in recycling facilities — none of which apply to robots. They can work around the clock, doing the work nobody else wants to do with greater precision and speed. They only shut down during downtime, which is reduced by preventive and predictive maintenance.

Comparing manual sorting efficacy outside the MRF is even more useful in validating the robotic potential to overcome human error. When nations began implementing recycling, the sorting burden was on the customer. Several bins sat on the curb for each waste type, and sometimes, there were designated vehicles for each category — a waste of fuel and energy. Eventually, single-stream recycling became the norm to increase consumer volume. However, this made sorting the MRFs responsible.

Exhausting manual bulk sorting led to high rates of human error and oversight. Around 10%-30% of recyclables still go to landfills, likely due to inaccurate sorting, misunderstood material type, or workplace apathy.

What Makes Recycling Robots Work?

Recycling robots are more adept than humans because of the endless work of engineers optimizing their three basic elements — their eyes, arms, and brains. Combining them with AI makes them even more skilled.


Sensors and cameras are the eyes, enabled by computer vision. This is a constantly advancing feature as robotics developers strive to make them more nuanced and exact, as long as the lighting conditions provide enough clarity. Understanding the value of complementary vision technologies like LED lights makes operations smoother because 30% of its brightness doesn’t fade in the first year like traditional bulbs. Computer vision is capable of identifying recycling waste based on metrics like:

  • Color
  • Shape
  • Size
  • Material

Modern advancements further inform them with machine learning and enhanced neural networks. Eventually, robotic eyes will distinguish label variants, material opacities, contamination levels, and original consumer applications, using infrared and hyperspectral imaging to see more colors than the human eye. The technology will benefit finicky plastics like HDPE and PET, typically in detergent or water bottles.

Trained robots will know how much shampoo is in a bottle before sorting it, potentially sending it for cleaning before processing to increase recovery rates. How much recycled waste is recycled varies from 40%-90% depending on the location and facility, and advanced robotic sight will make these numbers less variable. These details are necessary to advance robotics from a software perspective.


Robotic arms are sometimes articulated, which means they operate similarly to human limbs. The links between segments make them swift and flexible. They have the advantage of having linear or revolving joints, expanding their accessibility.

Pick-and-place arms are another variant with plug-and-play attachments based on volume type. Two-finger and vacuum-cup grippers are designed to grab materials differently based on their dimensions and weight. Machines, such as strong yet slow Cartesians or lightning-fast Delta-type arms, come in various shapes and sizes.


Advanced recycling robots have AI backing their operations, with engineers and data scientists controlling software to make them more effective. While the eyes and arms appear to do the literal and metaphorical heavy lifting, the data and training direct the robot’s components to make accurate decisions.

How Will Recycling Robots Get Better?

Training neural networks and algorithms are the most challenging yet invigorating obstacle in perfecting recycling robots. The sheer variety of shapes, sizes, and deformations requires near-perfect data coherence, primarily as recycling waste travels down conveyors quickly. Adaptations may include using image analysis and logical categorization to make informed decisions.

For example, a photo eye sensor can learn what a Starbucks logo or peanut looks like. Why does this matter? Machine learning can make connections between seemingly disparate data points. It can associate Starbucks imagery with specific materials and contamination qualities, placing it in a more appropriate chute.

However, the training becomes complex, as peanuts appear on more types of products than peanut jars. What about plastic wrappers and cardboard boxes? Finagling data requires finessing, but finding the sweet spot could amplify recovery rates.

AI-informed robotic equipment for MRFs can cost up to $300,000 per unit, making them hard to justify compared to a salary. As adoption spreads, their price will lower. Recycling robots will only get more compact and quicker to install, making them more cost-effective and likely to advance circular economies and make recycling profitable.

It must become cheaper to recycle than to put waste in landfills. Corporations need incentives to invest in advanced infrastructure like robotics. New York is a great example of why businesses choose the dump — 1 ton of recycling costs $686 versus $126 for landfills. This varies from state to state, but making disposal more expensive is the goal. MRFs must remember they can recover some of these costs by selling recyclable inventory.

What Industrial Shifts Will Recycling Robots Cause?

The more attentive and data-driven MRFs become, the better they communicate with corporations. Recycling facilities have an opportunity to become information hot spots, where they have near-infinite process discovery suggestions for organizations. For example, if a specific company’s packaging consistently goes to landfills, the MRF may start productive discussions with the enterprise to develop more eco-friendly packaging.

Innovative packaging types appear yearly, and companies change their products constantly. This requires constant training and retraining of robotic AI datasets. Instead of wasting energy and resources to redo this laborious process, MRFs can talk to specific agencies to find a more productive way to package products that increase recycling rates and not confuse customers. MRFs could convince organizations to do this as extended producer responsibility laws become more widespread and enforceable.

The recycling industry can operate on a positive or negative sorting process. Negative sorting is standard, where machines pick out what isn’t recyclable and let the rest pass into sorting bins for handling. Positive sorting is more advantageous because it targets what is viable instead. Negative processes often require a second manual procedure to confirm sorting, whereas positive methods are more likely to be categorized on a first pass.

This increases output purity and reduces the number of pieces going to landfills. Data analysts discover algorithmic errors over time. What do the robots sort incorrectly most often? Are they not adept at distinguishing a specific metric? Positive sorting makes it straightforward to identify how to improve the technologies.

Algorithmic Brains Streamline Waste Management

Robots are here to make Earth sustainable and help humans with unappealing jobs. Why shouldn’t MRFs take advantage of AI and machinery to rid the planet of waste and lead to a more circular economy?

The transition allows manual laborers to expand their skills to make an even more notable difference in climate change within the recycling sector. Eventually, recovery and purity rates will hit 100% because of the collaboration between human and robotic eyes.