Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning
This addresses the challenge of tool-use learning for robots, but it appears incremental as it builds on existing deep learning approaches without claiming major breakthroughs.
The paper tackled the problem of enabling robots to detect features of tools, objects, and actions from manipulation effects, using a deep learning model trained on sensory-motor data, and confirmed the robot's capability in generating predictions for unknown tools and objects.
We propose a tool-use model that can detect the features of tools, target objects, and actions from the provided effects of object manipulation. We construct a model that enables robots to manipulate objects with tools, using infant learning as a concept. To realize this, we train sensory-motor data recorded during a tool-use task performed by a robot with deep learning. Experiments include four factors: (1) tools, (2) objects, (3) actions, and (4) effects, which the model considers simultaneously. For evaluation, the robot generates predicted images and motions given information of the effects of using unknown tools and objects. We confirm that the robot is capable of detecting features of tools, objects, and actions by learning the effects and executing the task.