10,603 research outputs found
MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning
This paper describes a framework called MaestROB. It is designed to make the
robots perform complex tasks with high precision by simple high-level
instructions given by natural language or demonstration. To realize this, it
handles a hierarchical structure by using the knowledge stored in the forms of
ontology and rules for bridging among different levels of instructions.
Accordingly, the framework has multiple layers of processing components;
perception and actuation control at the low level, symbolic planner and Watson
APIs for cognitive capabilities and semantic understanding, and orchestration
of these components by a new open source robot middleware called Project Intu
at its core. We show how this framework can be used in a complex scenario where
multiple actors (human, a communication robot, and an industrial robot)
collaborate to perform a common industrial task. Human teaches an assembly task
to Pepper (a humanoid robot from SoftBank Robotics) using natural language
conversation and demonstration. Our framework helps Pepper perceive the human
demonstration and generate a sequence of actions for UR5 (collaborative robot
arm from Universal Robots), which ultimately performs the assembly (e.g.
insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018.
Video: https://www.youtube.com/watch?v=19JsdZi0TW
Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems
As robotic systems are moved out of factory work cells into human-facing
environments questions of choreography become central to their design,
placement, and application. With a human viewer or counterpart present, a
system will automatically be interpreted within context, style of movement, and
form factor by human beings as animate elements of their environment. The
interpretation by this human counterpart is critical to the success of the
system's integration: knobs on the system need to make sense to a human
counterpart; an artificial agent should have a way of notifying a human
counterpart of a change in system state, possibly through motion profiles; and
the motion of a human counterpart may have important contextual clues for task
completion. Thus, professional choreographers, dance practitioners, and
movement analysts are critical to research in robotics. They have design
methods for movement that align with human audience perception, can identify
simplified features of movement for human-robot interaction goals, and have
detailed knowledge of the capacity of human movement. This article provides
approaches employed by one research lab, specific impacts on technical and
artistic projects within, and principles that may guide future such work. The
background section reports on choreography, somatic perspectives,
improvisation, the Laban/Bartenieff Movement System, and robotics. From this
context methods including embodied exercises, writing prompts, and community
building activities have been developed to facilitate interdisciplinary
research. The results of this work is presented as an overview of a smattering
of projects in areas like high-level motion planning, software development for
rapid prototyping of movement, artistic output, and user studies that help
understand how people interpret movement. Finally, guiding principles for other
groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for
the 21st Century)"
http://www.mdpi.com/journal/arts/special_issues/Machine_Artis
Hand and Arm Gesture-based Human-Robot Interaction: A Review
The study of Human-Robot Interaction (HRI) aims to create close and friendly
communication between humans and robots. In the human-center HRI, an essential
aspect of implementing a successful and effective HRI is building a natural and
intuitive interaction, including verbal and nonverbal. As a prevalent
nonverbally communication approach, hand and arm gesture communication happen
ubiquitously in our daily life. A considerable amount of work on gesture-based
HRI is scattered in various research domains. However, a systematic
understanding of the works on gesture-based HRI is still lacking. This paper
intends to provide a comprehensive review of gesture-based HRI and focus on the
advanced finding in this area. Following the stimulus-organism-response
framework, this review consists of: (i) Generation of human gesture(stimulus).
(ii) Robot recognition of human gesture(organism). (iii) Robot reaction to
human gesture(response). Besides, this review summarizes the research status of
each element in the framework and analyze the advantages and disadvantages of
related works. Toward the last part, this paper discusses the current research
challenges on gesture-based HRI and provides possible future directions.Comment: 10 pages, 1 figure
Prediction of intent in robotics and multi-agent systems.
Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches
Object Transfer Point Estimation for Prompt Human to Robot Handovers
Handing over objects is the foundation of many human-robot interaction and collaboration tasks. In the scenario where a human is handing over an object to a robot, the human chooses where the object needs to be transferred. The robot needs to accurately predict this point of transfer to reach out proactively, instead of waiting for the final position to be presented. We first conduct a human-to-robot handover motion study to analyze the effect of user height, arm length, position, orientation and robot gaze on the object transfer point. Our study presents new observations on the effect of robot\u27s gaze on the point of object transfer. Next, we present an efficient method for predicting the Object Transfer Point (OTP), which synthesizes (1) an offline OTP calculated based on human preferences observed in the human-robot motion study with (2) a dynamic OTP predicted based on the observed human motion. Our proposed OTP predictor is implemented on a humanoid nursing robot and experimentally validated in human-robot handover tasks. Compared to using only static or dynamic OTP estimators, it has better accuracy at the earlier phase of handover (up to 45% of the handover motion) and can render fluent handovers with a reach-to-grasp response time (about 3.1 secs) close to natural human receiver\u27s response. In addition, the OTP prediction accuracy is maintained across the robot\u27s visible workspace by utilizing a user-adaptive reference frame
How to Communicate Robot Motion Intent: A Scoping Review
Robots are becoming increasingly omnipresent in our daily lives, supporting
us and carrying out autonomous tasks. In Human-Robot Interaction, human actors
benefit from understanding the robot's motion intent to avoid task failures and
foster collaboration. Finding effective ways to communicate this intent to
users has recently received increased research interest. However, no common
language has been established to systematize robot motion intent. This work
presents a scoping review aimed at unifying existing knowledge. Based on our
analysis, we present an intent communication model that depicts the
relationship between robot and human through different intent dimensions
(intent type, intent information, intent location). We discuss these different
intent dimensions and their interrelationships with different kinds of robots
and human roles. Throughout our analysis, we classify the existing research
literature along our intent communication model, allowing us to identify key
patterns and possible directions for future research.Comment: Interactive Data Visualization of the Paper Corpus:
https://rmi.robot-research.d
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