Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning
This work addresses labor pressures in the strawberry industry by improving machine perception for harvesting, though it is incremental as it focuses on a sub-objective within a broader unsolved problem.
The researchers tackled the problem of viewpoint optimization for autonomous strawberry harvesting by developing a deep reinforcement learning algorithm, which achieved 8.7 times higher returns than random actions and 8.8% faster exploration than a baseline visual servoing policy in simulation.
Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States's strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and commercially adopted strawberry harvesting system remains elusive. In this research, we explore the feasibility of using deep reinforcement learning to overcome these bottlenecks and develop a practical algorithm to address the sub-objective of viewpoint optimization, or the development of a control policy to direct a camera to favorable vantage points for autonomous harvesting. We evaluate the algorithm's performance in a custom, open-source simulated environment and observe encouraging results. Our trained agent yields 8.7 times higher returns than random actions and 8.8 percent faster exploration than our best baseline policy, which uses visual servoing. Visual investigation shows the agent is able to fixate on favorable viewpoints, despite having no explicit means to propagate information through time. Overall, we conclude that deep reinforcement learning is a promising area of research to advance the state of the art in autonomous strawberry harvesting.