1,846 research outputs found
Learning by observation through system identification
In our previous works, we present a new method
to program mobile robots āācode identification by
demonstrationāā based on algorithmically transferring
human behaviours to robot control code using
transparent mathematical functions. Our approach
has three stages: i) first extracting the trajectory of the
desired behaviour by observing the human, ii) making
the robot follow the human trajectory blindly to
log the robotās own perception perceived along that
trajectory, and finally iii) linking the robotās perception
to the desired behaviour to obtain a generalised,
sensor-based model.
So far we used an external, camera based motion
tracking system to log the trajectory of the human
demonstrator during his initial demonstration of the
desired motion. Because such tracking systems are
complicated to set up and expensive, we propose an alternative method to obtain trajectory information, using the robotās own sensor perception.
In this method, we train a mathematical polynomial using the NARMAX system identification methodology which maps the position of the āred jacketā worn by the demonstrator in the image captured by the robotās camera, to the relative position of the demonstrator in the real world according to the robot.
We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve door traversal behaviour
Robot training using system identification
This paper focuses on developing a formal, theory-based design methodology to generate transparent robot control programs using mathematical functions. The research finds its theoretical roots in robot training and system identification techniques such as Armax (Auto-Regressive Moving Average models with eXogenous inputs) and Narmax (Non-linear Armax). These techniques produce linear and non-linear polynomial functions that model the relationship between a robotās sensor perception and motor response.
The main benefits of the proposed design methodology, compared to the traditional robot programming techniques are: (i) It is a fast and efficient way of generating robot control code, (ii) The generated robot control programs are transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour, and (iii) It requires very little explicit knowledge of robot programming where end-users/programmers who do not have any specialised robot programming skills can nevertheless generate task-achieving sensor-motor couplings.
The nature of this research is concerned with obtaining sensor-motor couplings, be it through human demonstration via the robot, direct human demonstration, or other means. The viability of our methodology has been demonstrated by teaching various mobile robots different sensor-motor tasks such as wall following, corridor passing, door traversal and route learning
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
Robot programming by demonstration through system identification
Increasingly, personalised robots ā robots especially
designed and programmed for an individualās needs
and preferences ā are being used to support humans in
their daily lives, most notably in the area of service robotics. Arguably, the closer the robot is programmed to the individualās needs, the more useful it is, and we believe that giving people the opportunity to program their own robots, rather than programming robots for them, will push robotics research one step further in the personalised robotics field. However, traditional robot programming techniques require specialised technical skills from different disciplines and it is not reasonable to expect end-users to have these skills. In this paper, we therefore present a new method of obtaining robot control code ā programming by demonstration through system identification which algorithmically and automatically transfers human behaviours into robot control code, using transparent, analysable mathematical functions. Besides providing a simple means of generating perception-action mappings, they have the additional advantage that can also be used to form hypotheses and theoretical analysis of robot behaviour. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve wall following and corridor passing behaviours
Multisensor-based human detection and tracking for mobile service robots
The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms.
Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments
A bank of unscented Kalman filters for multimodal human perception with mobile service robots
A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints.
In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot.
Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
Torso detection and tracking using a 2D laser range finder
Detecting and tracking people in populated environments has various applications including, robotics, healthcare, automotive, security and defence. In this paper, we present an algorithm for people detection and tracking based on a two dimensional laser rage finder (LRF). The LRF was mounted on a mobile robotic platform to scan a torso section of a person. The tracker is designed to discard spurious targets based on the log likelihood ratio and can effectively handle short term occlusions. Long term occlusions are considered as new tracks. Performance of the algorithm is analysed based on experiments, which shows appealing results
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