Active Clothing Material Perception using Tactile Sensing and Deep Learning

Humans represent and discriminate the objects in the same category using their properties, and an intelligent robot should be able to do the same. In this paper, we build a robot system that can autonomously perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. Those properties include the physical properties, like thickness, fuzziness, softness and durability, and semantic properties, like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616 robot exploring iterations on them. To extract the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can autonomously explore the unknown clothes and learn their properties. This work proposes a new framework for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.Comment: ICRA 2018 accepte

InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions

Ambiguity is ubiquitous in human communication. Previous approaches in Human-Robot Interaction (HRI) have often relied on predefined interaction templates, leading to reduced performance in realistic and open-ended scenarios. To address these issues, we present a large-scale dataset, \invig, for interactive visual grounding under language ambiguity. Our dataset comprises over 520K images accompanied by open-ended goal-oriented disambiguation dialogues, encompassing millions of object instances and corresponding question-answer pairs. Leveraging the \invig dataset, we conduct extensive studies and propose a set of baseline solutions for end-to-end interactive visual disambiguation and grounding, achieving a 45.6\% success rate during validation. To the best of our knowledge, the \invig dataset is the first large-scale dataset for resolving open-ended interactive visual grounding, presenting a practical yet highly challenging benchmark for ambiguity-aware HRI. Codes and datasets are available at: \href{https://openivg.github.io}{https://openivg.github.io}.Comment: 8 pages, 9 figures, 3 tables, under revie

Non-meritocrats or conformist meritocrats? A redistribution experiment in China and France

Recent empirical evidence contends that meritocratic ideals are mainly a Western phenomenon. Intriguingly, the Chinese people appear to not differentiate between merit- and luck-based inequalities, despite their rich historical legacy of meritocratic institutions. We propose that this phenomenon might be due to the Chinese public’s greater adherence towards the status quo. In order to test this hypothesis, we run an incentivized redistribution experiment with elite university students in China and France, by varying the initial split of payoffs between two real-life workers to redistribute from. We show that Chinese respondents consistently and significantly choose more non-redistribution (playing the status quo) across both highly unequal and relatively equal status quo scenarios than our French respondents. Additionally, we also show that the Chinese sample does differentiate between merit- and luck-based inequalities, and does not redistribute less than the French absent status quo conformity. Ultimately, we contend that such a phenomenon is indicative of low political agency rather than apathy, inattention, or libertarian beliefs among the Chinese. Notably, our findings show that Chinese individuals’ conformity to the status quo is particularly pronounced among those from families of working-class and farming backgrounds, while it is conspicuously absent among individuals whose families have closer ties to the private sector

GPU-accelerated subgraph enumeration on partitioned graphs

Ministry of Education, Singapore under its Academic Research Funding Tier