Learning to Singulate Objects using a Push Proposal Network

Learning to act in unstructured environments, such as cluttered piles of objects, poses a substantial challenge for manipulation robots. We present a novel neural network-based approach that separates unknown objects in clutter by selecting favourable push actions. Our network is trained from data collected through autonomous interaction of a PR2 robot with randomly organized tabletop scenes. The model is designed to propose meaningful push actions based on over-segmented RGB-D images. We evaluate our approach by singulating up to 8 unknown objects in clutter. We demonstrate that our method enables the robot to perform the task with a high success rate and a low number of required push actions. Our results based on real-world experiments show that our network is able to generalize to novel objects of various sizes and shapes, as well as to arbitrary object configurations. Videos of our experiments can be viewed at http://robotpush.cs.uni-freiburg.deComment: International Symposium on Robotics Research (ISRR) 2017, videos: http://robotpush.cs.uni-freiburg.d

A computer vision model for visual-object-based attention and eye movements

This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.National Natural Science of Founda- tion of Chin

Deep Object-Centric Representations for Generalizable Robot Learning

Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a method where general purpose pretrained visual models serve as an object-centric prior for the perception system of a learned policy. We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy. A task-independent meta-attention locates possible objects in the scene, and a task-specific attention identifies which objects are predictive of the trajectories. The scope of the task-specific attention is easily adjusted by showing demonstrations with distractor objects or with diverse relevant objects. Our results indicate that this approach exhibits good generalization across object instances using very few samples, and can be used to learn a variety of manipulation tasks using reinforcement learning

Cutting through the clutter: Searching for targets in evolving complex scenes

We evaluated the use of visual clutter as a surrogate measure of set size effects in visual search by comparing the effects of subjective clutter (determined by independent raters) and objective clutter (as quantified by edge count and feature congestion) using evolving scenes, ones that varied incrementally in clutter while maintaining their semantic continuity. Observers searched for a target building in rural, suburban, and urban city scenes created using the game SimCity. Stimuli were 30 screenshots obtained for each scene type as the city evolved over time. Reaction times and search guidance (measured by scan path ratio) were fastest/strongest for sparsely cluttered rural scenes, slower/weaker for more cluttered suburban scenes, and slowest/weakest for highly cluttered urban scenes. Subjective within-city clutter estimates also increased as each city matured and correlated highly with RT and search guidance. However, multiple regression modeling revealed that adding objective estimates failed to better predict search performance over the subjective estimates alone. This suggests that within-city clutter may not be explained exclusively by low-level feature congestion; conceptual congestion (e.g., the number of different types of buildings in a scene), part of the subjective clutter measure, may also be important in determining the effects of clutter on search