Robots are becoming more prevalent in our daily lives, from automatic vacuum cleaners to drones that deliver packages. We are witnessing a growth in its ability to handle complex tasks as technology advances. They are beginning to perform the tasks that were once limited only to human capabilities.
One of those tasks is grabbing objects in dynamic and unpredictable environments, like picking a cherry from a tree. The branch is not stable, the wind is unpredictable, and the cherry is a tiny object for a robot. This is an extremely challenging task for a robot, as it is used to operating in environments with rigid surface support, such as in a factory where certain objects pass through a stable band.
Fine handling of small objects it is a challenging task for robots due to perceptual errors, sensor noise, and the inherently dynamic nature of the problem. On the other hand, it is a ubiquitous task in many fields, including manufacturing, healthcare and agriculture, and its automation could have immense practical and economic value.
When we think of a robot for a predetermined task, such as those used on assembly lines in factories, it is possible to design specific hardware for the given task. By analyzing the assembly process and the tools required, engineers can develop a robot design that can efficiently solve the problem at hand. This approach is effective because the robot is not designed to be used in other factories and the objects it interacts with will not change within the factory environment. However, the story changes when we want to reach a universal solution.
Suppose we need to develop a robot that can grab objects in different environments without any limitations. We know that the environment and objects will be dynamic. Is it still possible to develop a robot that can accurately grasp objects without stable support? This is the question the authors asked themselves, and they came up with cherrybot.
cherrybot is a dynamic system for fine manipulation that learns behavior by pre-training in a rough simulation and then fitting with modelless RL in the real world. It is designed to be precise enough to handle the task successfully while being robust against misperception and sensor noise. Also, it can handle dynamic scenarios like changing environments, moving objects, etc. Also, it can generalize well to objects with different sizes, shapes, and textures without the need for specific hardware.
cherrybot It takes advantage of the imperfect information accessible in most robots, such as an inaccurate simulator and a heuristics-based baseline policy, to start training RL to be surprisingly sample-efficient for real-world manipulation. Properly dynamic training tasks are designed to minimize human effort in the training process and allow for significantly stronger policies. The action space is designed to efficiently balance the manageability of learning with reactivity. The system is designed to accommodate plug-and-play perception modules and adapt to different objects and scenarios.
cherrybot uses generic hardware. An assembled robotic arm and chopsticks. That’s all. Chopsticks are used for fine manipulation. The robotic arm isn’t perfect either. It may provide inaccurate sensor results from time to time. Despite these drawbacks, cherrybot demonstrates superhuman reactivity in dynamic, high-precision tasks, such as using chopsticks to catch a slippery ball swinging in the air, after just 30 minutes of real-world interaction.
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Ekrem Çetinkaya received his B.Sc. in 2018 and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She is currently pursuing a PhD. She graduated from the University of Klagenfurt, Austria, and working as a researcher in the ATHENA project. Her research interests include deep learning, computer vision, and multimedia networks.
