Dexterous Robotic Manipulation with Adaptive Multi-Shot Generation of Fingertips

What is it about?

This work is about giving robots the “know-how” to pick up unfamiliar objects in tricky, ever-changing situations without having to reprogram them each time. First, our system trains to learn the overall shape of objects and the flexible parts of a robot’s gripper, but without any strict rules. Then, when it’s time for the robot to actually grab something in the real world, a separate step adjusts the AI’s solution to obey whatever safety or design rules are needed. That means the robot can explore lots of solutions during training but still end up with a grasp that’s guaranteed to be safe and practical. By splitting the learning from the real-world constraints, we can create more adaptive robots that don’t require constant reprogramming and that can work in uncertain environments.

Featured Image

Photo by Sufyan on Unsplash

Photo by Sufyan on Unsplash

Why is it important?

What makes this work stand out is the adaptability that the pipeline offers in unknown environments and conditions, by integrating gripper, target, and environmental conditions on the decision-making process of the neural networks. Because of the extra parameters, and the constraint-free training the model does not lock on the limits during the training phase, allowing us to further expand the graspable objects collection and the adaptability to new environments "on the fly".