語用論(Pragmatics)を考える

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Referring as a Collaborative Process

Documentation created as part of the Perceptual Form of the City, a research project investigating the individual’s perception of the urban landscape. Includes a review of techniques used, general critique and future proposals.

Notes on Methodology

Cobot programming for collaborative industrial tasks: An overview

In this paper we explore how social gaze in an assembly robot affects how naive users interact with it. In a controlled experimental study, 30 participants instructed an industrial robot to fetch parts needed to assemble a wooden toolbox. Participants either interacted with a robot employing a simple gaze following the movements of its own arm, or with a robot that follows its own movements during tasks, but which also gazes at the participant between instructions. Our qualitative and quantitative analyses show that people in the social gaze condition are significantly more quick to engage the robot, smile significantly more often, and can better account for where the robot is looking. In addition, we find people in the social gaze condition to feel more responsible for the task performance. We conclude that social gaze in assembly scenarios fulfills floor management functions and provides an indicator for the robot’s affordance, yet that it does not influence likability, mutual interest and suspected competence of the robot.

/pdf/the-effects-of-social-gaze-in-human-robot-collaborative-2gn8pkmqtl.pdf

The Effects of Social Gaze in Human-Robot Collaborative Assembly

Deictic gestures - pointing at things in human-human collaborative tasks - constitute a pervasive, non-verbal way of communication, used e.g. to direct attention towards objects of interest. In a human-robot interactive scenario, in order to delegate tasks from a human to a robot, one of the key requirements is to recognize and estimate the pose of the pointing gesture. Standard approaches rely on full-body or partial-body postures to detect the pointing direction. We present a probabilistic, appearance-based object detection framework to detect pointing gestures and robustly estimate the pointing direction. Our method estimates the pointing direction without assuming any human kinematic model. We propose a functional model for pointing which incorporates two types of pointing, finger pointing and tool pointing using an object in hand. We evaluate our method on a new dataset with 9 participants pointing at 10 objects.

/pdf/probabilistic-detection-of-pointing-directions-for-human-5gjsdhpcs9.pdf

Probabilistic Detection of Pointing Directions for Human-Robot Interaction

Understanding semantic meaning from hand gestures is a challenging but essential task in human-robot interaction scenarios. In this paper we present a baseline evaluation of the Innsbruck Multi-View Hand Gesture (IMHG) dataset [1] recorded with two RGB-D cameras (Kinect). As a baseline, we adopt a probabilistic appearance-based framework [2] to detect a hand gesture and estimate its pose using two cameras. The dataset consists of two types of deictic gestures with the ground truth location of the target, two symbolic gestures, two manipulative gestures, and two interactional gestures. We discuss the effect of parallax due to the offset between head and hand while performing deictic gestures. Furthermore, we evaluate the proposed framework to estimate the potential referents on the Innsbruck Pointing at Objects (IPO) dataset [2].

A multi-view hand gesture RGB-D dataset for human-robot interaction scenarios

We propose an approach to multi-view object detection and pose estimation that considers combinations of single-view estimates. It can be used with most existing single-view pose estimation systems, and can produce improved results even if the individual pose estimates are incoherent. The method is introduced in the context of an existing, probabilistic, view-based detection and pose estimation method (PAPE), which we here extend to incorporate diverse attributes of the scene. We tested the multiview approach with RGB-D cameras in different environments containing several cluttered test scenes and various textured and textureless objects. The results show that the accuracies of object detection and pose estimation increase significantly over single-view PAPE and over other multiple-view integration methods.

Integration of Probabilistic Pose Estimates from Multiple Views

Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behaviour for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human’s intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions.

/pdf/learning-semantics-of-gestural-instructions-for-human-robot-28as4h42bi.pdf

Dadhichi Shukla

Papers

The Effects of Social Gaze in Human-Robot Collaborative Assembly

Probabilistic Detection of Pointing Directions for Human-Robot Interaction

A multi-view hand gesture RGB-D dataset for human-robot interaction scenarios

Integration of Probabilistic Pose Estimates from Multiple Views

Learning Semantics of Gestural Instructions for Human-Robot Collaboration.