Namiko Saito, Tetsuya Ogata, Satoshi Funabashi, Hiroki Mori and Shigeki Sugano (Waseda University, Japan)
ICRA 2021 Best Paper in Cognitive Robotics
doi: 10.1109/LRA.2021.3062004
Abstract
Selection of appropriate tools and use of them when performing daily tasks is a critical function for introducing robots for domestic applications. In previous studies, however, adaptability to target objects was limited, making it difficult to accordingly change tools and adjust actions. To manipulate various objects with tools, robots must both understand tool functions and recognize object characteristics to discern a tool–object–action relation. We focus on active perception using multimodal sensorimotor data while a robot interacts with objects, and allow the robot to recognize their extrinsic and intrinsic characteristics. We construct a deep neural networks (DNN) model that learns to recognize object characteristics, acquires tool–object–action relations, and generates motions for tool selection and handling. As an example tool-use situation, the robot performs an ingredients transfer task, using a turner or ladle to transfer an ingredient from a pot to a bowl. The results confirm that the robot recognizes object characteristics and servings even when the target ingredients are unknown. We also examine the contributions of images, force, and tactile data and show that learning a variety of multimodal information results in rich perception for tool use.
Approach
Construct a deep neural networks (DNN) model to learn object characteristics recognition, tool selection, and tool handling in a series
Control the robot to recognize extrinsic and intrinsic characteristics of objects via active perception and acquires tool-object-action relations in the latent space
Presentation Video
Short (3 min) ver: Summary of work and results
Long (12 min) ver: Full presentation
