362 research outputs found
Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control
Designing a safe, trusted, and ethical AI may be practically impossible;
however, designing AI with safe, trusted, and ethical use in mind is possible
and necessary in safety and mission-critical domains like aerospace. Safe,
trusted, and ethical use of AI are often used interchangeably; however, a
system can be safely used but not trusted or ethical, have a trusted use that
is not safe or ethical, and have an ethical use that is not safe or trusted.
This manuscript serves as a primer to illuminate the nuanced differences
between these concepts, with a specific focus on applications of Human-AI
teaming in aerospace system control, where humans may be in, on, or
out-of-the-loop of decision-making
Revealing the ‘face’ of the robot introducting the ethics of Levinas to the field of robo-ethics
This paper explore the possibility of a new philosophical turn in robot-ethics, considering whether the concepts of Emanuel Levinas particularly his conception of the ‘face of the other’ can be used to understand how non-expert users interact with robots. The term ‘Robot’ comes from fiction and for non-experts and experts alike interaction with robots may be coloured by this history. This paper explores the ethics of robots (and the use of the term robot) that is based on the user seeing the robot as infinitely complex
TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization
Inter-process communication (IPC) is one of the core functions of modern
robotics middleware. We propose an efficient IPC technique called TZC (Towards
Zero-Copy). As a core component of TZC, we design a novel algorithm called
partial serialization. Our formulation can generate messages that can be
divided into two parts. During message transmission, one part is transmitted
through a socket and the other part uses shared memory. The part within shared
memory is never copied or serialized during its lifetime. We have integrated
TZC with ROS and ROS2 and find that TZC can be easily combined with current
open-source platforms. By using TZC, the overhead of IPC remains constant when
the message size grows. In particular, when the message size is 4MB (less than
the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens
of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2
IPC from hundreds of milliseconds to less than 1 millisecond. We also
demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in
autonomous driving scenarios. We show that by using TZC, the braking distance
can be shortened by 16% than ROS
Planning in constraint space for multi-body manipulation tasks
Robots are inherently limited by physical constraints on their link lengths, motor torques, battery
power and structural rigidity. To thrive in circumstances that push these limits, such as in search
and rescue scenarios, intelligent agents can use the available objects in their environment as
tools. Reasoning about arbitrary objects and how they can be placed together to create useful
structures such as ramps, bridges or simple machines is critical to push beyond one's physical
limitations. Unfortunately, the solution space is combinatorial in the number of available objects
and the configuration space of the chosen objects and the robot that uses the structure is high
dimensional.
To address these challenges, we propose using constraint satisfaction as a means to test the
feasibility of candidate structures and adopt search algorithms in the classical planning literature
to find sufficient designs. The key idea is that the interactions between the components of a
structure can be encoded as equality and inequality constraints on the configuration spaces of the
respective objects. Furthermore, constraints that are induced by a broadly defined action, such as
placing an object on another, can be grouped together using logical representations such as Planning
Domain Definition Language (PDDL). Then, a classical planning search algorithm can reason about
which set of constraints to impose on the available objects, iteratively creating a structure that
satisfies the task goals and the robot constraints. To demonstrate the effectiveness of this
framework, we present both simulation and real robot results with static structures such as ramps,
bridges and stairs, and quasi-static structures such as lever-fulcrum simple machines.Ph.D
PERSONHOOD FOR SYNTHETIC BEINGS: LEGAL PARAMETERS AND CONSEQUENCES OF THE DAWN OF HUMANLIKE ARTIFICIAL INTELLIGENCE
PERSONHOOD FOR SYNTHETIC BEINGS: LEGAL PARAMETERS AND CONSEQUENCES OF THE DAWN OF HUMANLIKEARTIFICIAL INTELLIGENC
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