With all the apples in this photo… how many days could you keep the doctor away for?

windfallen apples

A month? A year? More? We have some bad news… most of the apples in this photo will be wasted. In fact, thirty eight percent of all food in the US is wasted. This is about 145 billion meals.

We waste enough food to feed every person on food stamps for the entire year three times over.

A substantial amount of this waste happens at the farm level–a study by Santa Clara University found that field losses of edible produce are approximately 33.7% of marketed yield. Windfallen is a team of researchers passionate about reinforcement learning, robotics, agriculture and applying emergent technology to hard problems, such as reducing food waste at the farm level.

We are reimagining agriculture through the lens of robotics.

Advancements in robotic technology are primed to overhaul the agriculture industry, where 30-40% of farm operating costs come from labor. Employing laborers to harvest produce poses numerous challenges–one such challenge is that increasing labor costs are shrinking farmers’ margins, making the harvest and sale of certain lower-margin produce uneconomical. On a ripe apple tree, a fall breeze or a hot day can cause a large proportion of fruit to prematurely fall from the branches. In the past, these ‘windfall’ fruits were collected and sold at a lower margin for processed foodstuffs like cider and applesauce. Nowadays, more and more apples are left to rot on the ground because increasing labor costs do not justify their collection.

The collection of windfall apples, and other windfall fruit, is a promising area for robotic deployment to reduce food waste while not threatening workers’ livelihood. However, successful robotic deployment is a notoriously difficult task requiring multiple layers of hardware and software technology. Our team focuses on the specific layer of robot orchestration in an agricultural setting. We have simulated the operation of a multi-robot fleet that collaborates to collect windfall fruit through multi-agent reinforcement learning (MARL).

How do you get robots to work together toward a common goal?

As industrial automation continues, more robots will be deployed to carry out tasks. As this occurs, the problem of robotic orchestration is becoming increasingly important. In other words, how do you get robots to work together to achieve their goals?

The burgeoning field of multi-agent reinforcement learning (MARL) offers a promising machine-learning based approach to this problem. As the field of robotics approaches its GPT moment, the demand for MARL technology in real-world contexts is likely to grow.