5,363 research outputs found
A Case for Machine Ethics in Modeling Human-Level Intelligent Agents
This paper focuses on the research field of machine ethics and how it relates to a technological singularity—a hypothesized, futuristic event where artificial machines will have greater-than-human-level intelligence. One problem related to the singularity centers on the issue of whether human values and norms would survive such an event. To somehow ensure this, a number of artificial intelligence researchers have opted to focus on the development of artificial moral agents, which refers to machines capable of moral reasoning, judgment, and decision-making. To date, different frameworks on how to arrive at these agents have been put forward. However, there seems to be no hard consensus as to which framework would likely yield a positive result. With the body of work that they have contributed in the study of moral agency, philosophers may contribute to the growing literature on artificial moral agency. While doing so, they could also think about how the said concept could affect other important philosophical concepts
What is Robotics: Why Do We Need It and How Can We Get It?
Robotics is an emerging synthetic science concerned with programming work. Robot technologies are quickly advancing beyond the insights of the existing science. More secure intellectual foundations will be required to achieve better, more reliable and safer capabilities as their penetration into society deepens. Presently missing foundations include the identification of fundamental physical limits, the development of new dynamical systems theory and the invention of physically grounded programming languages. The new discipline needs a departmental home in the universities which it can justify both intellectually and by its capacity to attract new diverse populations inspired by the age old human fascination with robots.
For more information: Kod*la
From explanation to synthesis: Compositional program induction for learning from demonstration
Hybrid systems are a compact and natural mechanism with which to address
problems in robotics. This work introduces an approach to learning hybrid
systems from demonstrations, with an emphasis on extracting models that are
explicitly verifiable and easily interpreted by robot operators. We fit a
sequence of controllers using sequential importance sampling under a generative
switching proportional controller task model. Here, we parameterise controllers
using a proportional gain and a visually verifiable joint angle goal. Inference
under this model is challenging, but we address this by introducing an
attribution prior extracted from a neural end-to-end visuomotor control model.
Given the sequence of controllers comprising a task, we simplify the trace
using grammar parsing strategies, taking advantage of the sequence
compositionality, before grounding the controllers by training perception
networks to predict goals given images. Using this approach, we are
successfully able to induce a program for a visuomotor reaching task involving
loops and conditionals from a single demonstration and a neural end-to-end
model. In addition, we are able to discover the program used for a tower
building task. We argue that computer program-like control systems are more
interpretable than alternative end-to-end learning approaches, and that hybrid
systems inherently allow for better generalisation across task configurations
Representation recovers information
Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation-use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is *necessary* for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation-use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing
An autonomous satellite architecture integrating deliberative reasoning and behavioural intelligence
This paper describes a method for the design of autonomous spacecraft, based upon behavioral approaches to intelligent robotics. First, a number of previous spacecraft automation projects are reviewed. A methodology for the design of autonomous spacecraft is then presented, drawing upon both the European Space Agency technological center (ESTEC) automation and robotics methodology and the subsumption architecture for autonomous robots. A layered competency model for autonomous orbital spacecraft is proposed. A simple example of low level competencies and their interaction is presented in order to illustrate the methodology. Finally, the general principles adopted for the control hardware design of the AUSTRALIS-1 spacecraft are described. This system will provide an orbital experimental platform for spacecraft autonomy studies, supporting the exploration of different logical control models, different computational metaphors within the behavioral control framework, and different mappings from the logical control model to its physical implementation
Action Sequencing Using Visual Permutations
Humans can easily reason about the sequence of high level actions needed to
complete tasks, but it is particularly difficult to instil this ability in
robots trained from relatively few examples. This work considers the task of
neural action sequencing conditioned on a single reference visual state. This
task is extremely challenging as it is not only subject to the significant
combinatorial complexity that arises from large action sets, but also requires
a model that can perform some form of symbol grounding, mapping high
dimensional input data to actions, while reasoning about action relationships.
This paper takes a permutation perspective and argues that action sequencing
benefits from the ability to reason about both permutations and ordering
concepts. Empirical analysis shows that neural models trained with latent
permutations outperform standard neural architectures in constrained action
sequencing tasks. Results also show that action sequencing using visual
permutations is an effective mechanism to initialise and speed up traditional
planning techniques and successfully scales to far greater action set sizes
than models considered previously.Comment: This paper has been accepted for publication at IEEE RA-
Proceedings of the 1st Standardized Knowledge Representation and Ontologies for Robotics and Automation Workshop
Welcome to IEEE-ORA (Ontologies for Robotics and Automation) IROS workshop. This
is the 1st edition of the workshop on! Standardized Knowledge Representation and
Ontologies for Robotics and Automation. The IEEE-ORA 2014 workshop was held on
the 18th September, 2014 in Chicago, Illinois, USA.
In!the IEEE-ORA IROS workshop, 10 contributions were presented from 7 countries in
North and South America, Asia and Europe. The presentations took place in the
afternoon, from 1:30 PM to 5:00 PM. The first session was dedicated to “Standards for
Knowledge Representation in Robotics”, where presentations were made from the
IEEE working group standards for robotics and automation, and also from the ISO TC
184/SC2/WH7. The second session was dedicated to “Core and Application
Ontologies”, where presentations were made for core robotics ontologies, and also for
industrial and robot assisted surgery ontologies. Three posters were presented in
emergent applications of ontologies in robotics.
We would like to express our thanks to all participants. First of all to the authors,
whose quality work is the essence of this workshop. Next, to all the members of the
international program committee, who helped us with their expertise and valuable
time. We would also like to deeply thank the IEEE-IROS 2014 organizers for hosting
this workshop.
Our deep gratitude goes to the IEEE Robotics and Automation Society, that sponsors!
the IEEE-ORA group activities, and also to the scientific organizations that kindly
agreed to sponsor all the workshop authors work
- …