214 research outputs found
A review and comparison of ontology-based approaches to robot autonomy
Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft
Manipulation Planning and Control for Shelf Replenishment
Manipulation planning and control are relevant building blocks of a robotic
system and their tight integration is a key factor to improve robot autonomy
and allows robots to perform manipulation tasks of increasing complexity, such
as those needed in the in-store logistics domain. Supermarkets contain a large
variety of objects to be placed on the shelf layers with specific constraints,
doing this with a robot is a challenge and requires a high dexterity. However,
an integration of reactive grasping control and motion planning can allow
robots to perform such tasks even with grippers with limited dexterity. The
main contribution of the paper is a novel method for planning manipulation
tasks to be executed using a reactive control layer that provides more control
modalities, i.e., slipping avoidance and controlled sliding. Experiments with a
new force/tactile sensor equipping the gripper of a mobile manipulator show
that the approach allows the robot to successfully perform manipulation tasks
unfeasible with a standard fixed grasp.Comment: 8 pages, 12 figures, accepted at RA
Logic programming for deliberative robotic task planning
Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application
An AI-Based Goal-Oriented Agent for Advanced On-Board Automation
In the context of fierce competition arising in the space economy, the number of satellites and constellations that will be placed in orbit is set to increase considerably in the upcoming years. In such a dynamic environment, raising the autonomy level of the next space missions is key to maintaining a competitive edge in terms of the scientific, technological, and commercial outcome.
We propose the adoption of an AI-based autonomous agent aiming to fully enable spacecraft’s goal-oriented autonomy. The implemented cognitive architecture collects input starting from the sensing of the surrounding operating environment and defines a low-level schedule of tasks that will be carried out throughout the specified horizon. Furthermore, the agent provides a planner module designed to find optimal solutions that maximize the outcome of the pursued objective goal. The autonomous loop is closed by comparing the expected outcome of these scheduled tasks against the real environment measurements.
The entire algorithmic pipeline was tested in a simulated operational environment, specifically developed for replicating inputs and resources relative to Earth Observation missions. The autonomous reasoning agent was evaluated against the classical, non-autonomous, mission control approach, considering both the quantity and the quality of collected observation data in addition to the quantity of the observation opportunities exploited throughout the simulation time. The preliminary simulation results point out that the adoption of our software agent enhances dramatically the effectiveness of the entire mission, increasing and optimizing in-orbit activities, on the one hand, reducing events\u27 response latency (opportunities, failures, malfunctioning, etc.) on the other.
In the presentation, we will cover the description of the high-level algorithmic structure of the proposed goal-oriented reasoning model, as well as a brief explanation of each internal module’s contribution to the overall agent’s architecture. Besides, an overview of the parameters processed as input and the expected algorithms\u27 output will be provided, to contextualize the placement of the proposed solution. Finally, an Earth Observation use case will be used as the benchmark to test the performances of the proposed approach against the classical one, highlighting promising conclusions regarding our autonomous agent’s adoption
Cognitive Reasoning for Compliant Robot Manipulation
Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots
A Survey of Knowledge Representation in Service Robotics
Within the realm of service robotics, researchers have placed a great amount
of effort into learning, understanding, and representing motions as
manipulations for task execution by robots. The task of robot learning and
problem-solving is very broad, as it integrates a variety of tasks such as
object detection, activity recognition, task/motion planning, localization,
knowledge representation and retrieval, and the intertwining of
perception/vision and machine learning techniques. In this paper, we solely
focus on knowledge representations and notably how knowledge is typically
gathered, represented, and reproduced to solve problems as done by researchers
in the past decades. In accordance with the definition of knowledge
representations, we discuss the key distinction between such representations
and useful learning models that have extensively been introduced and studied in
recent years, such as machine learning, deep learning, probabilistic modelling,
and semantic graphical structures. Along with an overview of such tools, we
discuss the problems which have existed in robot learning and how they have
been built and used as solutions, technologies or developments (if any) which
have contributed to solving them. Finally, we discuss key principles that
should be considered when designing an effective knowledge representation.Comment: Accepted for RAS Special Issue on Semantic Policy and Action
Representations for Autonomous Robots - 22 Page
Towards a Neuronally Consistent Ontology for Robotic Agents
The Collaborative Research Center for Everyday Activity Science & Engineering
(CRC EASE) aims to enable robots to perform environmental interaction tasks
with close to human capacity. It therefore employs a shared ontology to model
the activity of both kinds of agents, empowering robots to learn from human
experiences. To properly describe these human experiences, the ontology will
strongly benefit from incorporating characteristics of neuronal information
processing which are not accessible from a behavioral perspective alone. We,
therefore, propose the analysis of human neuroimaging data for evaluation and
validation of concepts and events defined in the ontology model underlying most
of the CRC projects. In an exploratory analysis, we employed an Independent
Component Analysis (ICA) on functional Magnetic Resonance Imaging (fMRI) data
from participants who were presented with the same complex video stimuli of
activities as robotic and human agents in different environments and contexts.
We then correlated the activity patterns of brain networks represented by
derived components with timings of annotated event categories as defined by the
ontology model. The present results demonstrate a subset of common networks
with stable correlations and specificity towards particular event classes and
groups, associated with environmental and contextual factors. These neuronal
characteristics will open up avenues for adapting the ontology model to be more
consistent with human information processing.Comment: Preprint of paper accepted for the European Conference on Artificial
Intelligence (ECAI) 2023 (minor typo corrections
Peripersonal Space in the Humanoid Robot iCub
Developing behaviours for interaction with objects close to the body is a primary goal for any organism to survive in the world. Being able to develop such behaviours will be an essential feature in autonomous humanoid robots in order to improve their integration into human environments. Adaptable spatial abilities will make robots safer and improve their social skills, human-robot and robot-robot collaboration abilities.
This work investigated how a humanoid robot can explore and create action-based representations of its peripersonal space, the region immediately surrounding the body where reaching is possible without location displacement. It presents three empirical studies based on peripersonal space findings from psychology, neuroscience and robotics. The experiments used a visual perception system based on active-vision and biologically inspired neural networks.
The first study investigated the contribution of binocular vision in a reaching task. Results indicated the signal from vergence is a useful embodied depth estimation cue in the peripersonal space in humanoid robots. The second study explored the influence of morphology and postural experience on confidence levels in reaching assessment. Results showed that a decrease of confidence when assessing targets located farther from the body, possibly in accordance to errors in depth estimation from vergence for longer distances. Additionally, it was found that a proprioceptive arm-length signal extends the robot’s peripersonal space. The last experiment modelled development of the reaching skill by implementing motor synergies that progressively unlock degrees of freedom in the arm. The model was advantageous when compared to one that included no developmental stages.
The contribution to knowledge of this work is extending the research on biologically-inspired methods for building robots, presenting new ways to further investigate the robotic properties involved in the dynamical adaptation to body and sensing characteristics, vision-based action, morphology and confidence levels in reaching assessment.CONACyT, Mexico (National Council of Science and Technology
- …