Taskoriented Function Detection Based on Operational Tasks We propose novel representations for functions of an object, namely Taskoriented Function, which is improved upon the idea of Afforadance in the field of Robotics Vision. We also propose a convolutional neural network to detect taskoriented functions. This network takes as input an operational task as well as an RGB image and assign each pixel an appropriate label for every task. Taskoriented funciton makes it possible to descibe various ways to use an object because the outputs from the network differ depending on operational tasks. We introduce a new dataset for taskoriented function detection, which contains about 1200 RGB images and 6000 pixellevel annotations assuming five tasks. Our proposed method reached 0.80 mean IOU in our dataset.

The RGBD Triathlon: Towards Agile Visual Toolboxes for Robots Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others. Still, most approaches typically address visual tasks in isolation, resulting in overspecialized models which achieve strong performances in specific applications but work poorly in other (often related) tasks. This is clearly suboptimal for a robot which is often required to perform simultaneously multiple visual recognition tasks in order to properly act and interact with the environment. This problem is exacerbated by the limited computational and memory resources typically available onboard to a robotic platform. The problem of learning flexible models which can handle multiple tasks in a lightweight manner has recently gained attention in the computer vision community and benchmarks supporting this research have been proposed. In this work we study this problem in the robot vision context, proposing a new benchmark, the RGBD Triathlon, and evaluating state of the art algorithms in this novel challenging scenario. We also define a new evaluation protocol, better suited to the robot vision setting. Results shed light on the strengths and weaknesses of existing approaches and on open issues, suggesting directions for future research.