22,162 research outputs found
Spatial context-aware person-following for a domestic robot
Domestic robots are in the focus of research in
terms of service providers in households and even as robotic
companion that share the living space with humans. A major
capability of mobile domestic robots that is joint exploration
of space. One challenge to deal with this task is how could we
let the robots move in space in reasonable, socially acceptable
ways so that it will support interaction and communication
as a part of the joint exploration. As a step towards this
challenge, we have developed a context-aware following behav-
ior considering these social aspects and applied these together
with a multi-modal person-tracking method to switch between
three basic following approaches, namely direction-following,
path-following and parallel-following. These are derived from
the observation of human-human following schemes and are
activated depending on the current spatial context (e.g. free
space) and the relative position of the interacting human.
A combination of the elementary behaviors is performed in
real time with our mobile robot in different environments.
First experimental results are provided to demonstrate the
practicability of the proposed approach
The perception of emotion in artificial agents
Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents
Towards Active Event Recognition
Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision.We propose a probabilistic generative framework based on a mixture of Kalman filters and information gain maximisation that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration.Interestingly, the sensors-control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness.Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems
Reasoning About Liquids via Closed-Loop Simulation
Simulators are powerful tools for reasoning about a robot's interactions with
its environment. However, when simulations diverge from reality, that reasoning
becomes less useful. In this paper, we show how to close the loop between
liquid simulation and real-time perception. We use observations of liquids to
correct errors when tracking the liquid's state in a simulator. Our results
show that closed-loop simulation is an effective way to prevent large
divergence between the simulated and real liquid states. As a direct
consequence of this, our method can enable reasoning about liquids that would
otherwise be infeasible due to large divergences, such as reasoning about
occluded liquid.Comment: Robotics: Science & Systems (RSS), July 12-16, 2017. Cambridge, MA,
US
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
Visual Closed-Loop Control for Pouring Liquids
Pouring a specific amount of liquid is a challenging task. In this paper we
develop methods for robots to use visual feedback to perform closed-loop
control for pouring liquids. We propose both a model-based and a model-free
method utilizing deep learning for estimating the volume of liquid in a
container. Our results show that the model-free method is better able to
estimate the volume. We combine this with a simple PID controller to pour
specific amounts of liquid, and show that the robot is able to achieve an
average 38ml deviation from the target amount. To our knowledge, this is the
first use of raw visual feedback to pour liquids in robotics.Comment: To appear at ICRA 201
Averting Robot Eyes
Home robots will cause privacy harms. At the same time, they can provide beneficial servicesāas long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms.
We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology
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