Quantifying children's sensorimotor experience: Child body-object interaction ratings for 3,359 English words

Body-object interaction (BOI) ratings measure how easily the human body can physically interact with a word's referent. Previous research has found that words higher in BOI tend to be processed more quickly and accurately in tasks such as lexical decision, semantic decision, and syntactic classification, suggesting that sensorimotor information is an important aspect of lexical knowledge. However, limited research has examined the importance of sensorimotor information from a developmental perspective. One barrier to addressing such theoretical questions has been a lack of semantic dimension ratings that take into account child sensorimotor experience. The goal of the current study was to collect Child BOI rating norms. Parents of children aged 5 – 9-years-old were asked to rate words according to how easily an average 6-year-old child can interact with each word’s referent. The relationships of Child and Adult BOI ratings with other lexical semantic dimensions were assessed, as well as the relationships of Child and Adult BOI ratings with age of acquisition. Child BOI ratings were more strongly related to valence and sensory experience ratings than Adult BOI ratings and were a better predictor of three different measures of age of acquisition. The results suggest that child-centric ratings such as those reported here provide a more sensitive measure of children’s experience that can be used to address theoretical questions in embodied cognition from a developmental perspective.

Aerial Grasping and Transport Using an Unmanned Aircraft (UA) Equipped with an Industrial Suction Gripper

Duetotheavailabilityof highly efficient unmanned aircraft (UA) and the advancement of the necessary technologies, the use of UA for object manipulation and cargo transport is becoming a more and more relevant research area. A reliable identification and localization of cargo and interaction objects as well as maintaining the required flight precision are essential to guarantee a successful object handling. Within this paper we demonstrate the successful application of an autonomous UA equipped with a lightweight suction gripper for object interaction. We discuss the approach used for precise localization as well as the identification and pose estimation of individual gripping objects. Concluding, the overall system performance is evaluated within an industrial-oriented use case.

Planning for grasping cluttered objects based on obstruction degree

Grasping objects in clutter is more difficult than grasping a separated single object. An important issue is that unsafe grasps may occur, in case, one object sits or leans on another, which could cause the collapse of objects. In addition, reachability of each object surrounded by other obstacles also has to be considered. So the order of multiple objects for grasping and the grasp configuration of each object must be planned simultaneously. This article combines grasp order and grasp configuration planning to perform fast and safe multiobject grasping in cluttered scenes. First, a comprehensive grasp configuration database is built to provide enough feasible grasp configurations for the objects. Then, we propose an obstruction degree to estimate the likelihood of reachability of each grasp configuration as well as each object. This measurement also implicitly infers object interactions. Finally, grasp order and grasp configurations are planned together to deal with the constraints caused by reachability and object interaction. Simulations and experiments in a series of cluttered scenes demonstrate that our method can grasp objects efficiently and can greatly reduce unsafe grasps.

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human demonstrations, highlighting the main challenges in this research area and suggesting desirable future developments. To this extent, the article presents a general definition of the hand-object interaction problem together with a concise review for each of the main subproblems involved, namely: sensing, perception, and learning. Furthermore, the article discusses the interplay between these subproblems and describes how their interaction in learning from demonstration contributes to the success of robot manipulation. In this way, the article provides a broad overview of the interdisciplinary approaches necessary for a robotic system to learn new manipulation skills by observing human behavior in the real world.