2,568 research outputs found
Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
The success of the human-robot co-worker team in a flexible manufacturing
environment where robots learn from demonstration heavily relies on the correct
and safe operation of the robot. How this can be achieved is a challenge that
requires addressing both technical as well as human-centric research questions.
In this paper we discuss the state of the art in safety assurance, existing as
well as emerging standards in this area, and the need for new approaches to
safety assurance in the context of learning machines. We then focus on robotic
learning from demonstration, the challenges these techniques pose to safety
assurance and indicate opportunities to integrate safety considerations into
algorithms "by design". Finally, from a human-centric perspective, we stipulate
that, to achieve high levels of safety and ultimately trust, the robotic
co-worker must meet the innate expectations of the humans it works with. It is
our aim to stimulate a discussion focused on the safety aspects of
human-in-the-loop robotics, and to foster multidisciplinary collaboration to
address the research challenges identified
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Effect of Adapting to Human Preferences on Trust in Human-Robot Teaming
We present the effect of adapting to human preferences on trust in a
human-robot teaming task. The team performs a task in which the robot acts as
an action recommender to the human. It is assumed that the behavior of the
human and the robot is based on some reward function they try to optimize. We
use a new human trust-behavior model that enables the robot to learn and adapt
to the human's preferences in real-time during their interaction using Bayesian
Inverse Reinforcement Learning. We present three strategies for the robot to
interact with a human: a non-learner strategy, in which the robot assumes that
the human's reward function is the same as the robot's, a non-adaptive learner
strategy that learns the human's reward function for performance estimation,
but still optimizes its own reward function, and an adaptive-learner strategy
that learns the human's reward function for performance estimation and also
optimizes this learned reward function. Results show that adapting to the
human's reward function results in the highest trust in the robot.Comment: 6 pages, 6 figures, AAAI Fall Symposium on Agent Teaming in
Mixed-Motive Situation
Swarm-Based Techniques for Adaptive Navigation Primitives
Adaptive Navigation (AN) has, in the past, been successfully accomplished by using mobile multi-robot systems (MMS) in highly structured formations known as clusters. Such multi-robot adaptive navigation (MAN) allows for real-time reaction to sensor readings and navigation to a goal location not known a priori. This thesis successfully reproduces MAN cluster techniques via swarm control techniques, a less computationally expensive but less formalized control technique for MMS, which achieves robot control through a combination of primitive robot behaviors. While powerful for large numbers of robots, swarm robotics often relies on “emergent” swarm behaviors resulting from robot-level behaviors, rather than top-down specification of swarm behaviors. For adaptive navigation purposes, it was desired to be able to specify swarm-level behavior from a top down perspective rather than experimenting with emergent behaviors. To this end, a simulation environment was developed to allow rapid development and vetting of swarm behaviors while easily interfacing with an existing testbed for validation on hardware. An initial suite of robot primitive and composite behaviors was developed and vetted using this simulator, and the behaviors were validated using the existing testbed in Santa Clara University’s Robotics System Laboratory (RSL). Of particular importance were the adaptive navigation primitives of extrema finding and contour finding and following. These AN primitives were tested over a variety of experimental parameters, yielding design guidelines for top-down specification of swarm robotic adaptive navigation. These design guidelines are presented, and their usefulness is demonstrated for a Contour Finding and Following application using the RSL’s testbed. Finally, possible future work to expand the capability of swarm-based adaptive navigation techniques is discussed
Exploring AI-enhanced Shared Control for an Assistive Robotic Arm
Assistive technologies and in particular assistive robotic arms have the
potential to enable people with motor impairments to live a self-determined
life. More and more of these systems have become available for end users in
recent years, such as the Kinova Jaco robotic arm. However, they mostly require
complex manual control, which can overwhelm users. As a result, researchers
have explored ways to let such robots act autonomously. However, at least for
this specific group of users, such an approach has shown to be futile. Here,
users want to stay in control to achieve a higher level of personal autonomy,
to which an autonomous robot runs counter. In our research, we explore how
Artifical Intelligence (AI) can be integrated into a shared control paradigm.
In particular, we focus on the consequential requirements for the interface
between human and robot and how we can keep humans in the loop while still
significantly reducing the mental load and required motor skills.Comment: Workshop on Engineering Interactive Systems Embedding AI Technologies
(EIS-embedding-AI) at EICS'2
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