715 research outputs found
Robotics for Intralogistics in Supermarkets and Retail Stores
No full textThis book aims at sharing knowledge about the technological opportunities and the main research challenges regarding robotics for logistics in supermarkets and retail stores, from the perspectives of the end-users, logistic companies, technology providers, and robotic researchers. The authors have been involved into the H2020 project Robotics Enabling Fully Integrated Logistics Lines for Supermarkets (REFILLS), aimed at improving logistics in supermarkets thanks to mobile robotic systems in close and smart collaboration with humans. The readers will find a comprehensive analysis of the main logistic processes in retail stores with possible robotized solutions, involving mechanical design, perception, and control. These technologies have been validated in realistic environments, and some of them have been tested into real supermarkets. The book is intended for a broad academic and industrial readership, including operators in the field of logistics, distribution, and retail
CoTeSys—Cognition for Technical Systems
No full textThe CoTeSys cluster of excellence (Beetz et al. in Proceedings of the 30th German Conference on Artificial Intelligence, KI-2007, pp. 19–42, 2007) investigates cognition for technical systems such as robots and factories. Cognitive technical systems (CTS) are information processing systems equipped with artificial sensors and actuators, integrated and embedded into physical systems, and acting in a physical world. They differ from other technical systems as they perform cognitive control and have cognitive capabilities. Cognitive control orchestrates reflexive and habitual behavior in accord with longterm intentions. Cognitive capabilities such as perception, action, knowledge and models, reasoning, learning and planning turn technical systems into systems that “know what they are doing”. The cognitive capabilities result in systems of higher reliability, flexibility, adaptivity and better performance
09341 Abstracts Collection – Cognition, Control and Learning for Robot Manipulation in Human Environments
No full textFrom 16.08. to 21.08.2009, the Dagstuhl Seminar 09341 ``Cognition, Control and Learning for Robot Manipulation in Human Environments '' was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
09341 Summary – Cognition, Control and Learning for Robot Manipulation in Human Environments
No full textHigh performance robot arms are faster, more accurate, and stronger
than humans.
Yet many manipulation tasks that are easily performed by humans as
part of their daily life are well beyond the capabilities of such
robots. The main reason for this superiority is that humans can rely
upon neural information processing and control mechanisms which are
tailored for performing complex motor skills, adapting to uncertain
environments and to not imposing a danger to surrounding humans. As
we are working towards autonomous service robots operating and
performing manipulation in the presence of humans and in human living
and working environments, the robots must exhibit similar levels of
flexibility, compliance, and adaptivity.
The goal of this Dagstuhl seminar is to make a big step towards
pushing robot manipulation forward such that robot assisted living can
become a concrete vision for the future.
In order to achieve this goal, the computational aspects of everyday
manipulation tasks need to be well-understood, and requires the
thorough study of the interaction of
perceptual, learning, reasoning, planning, and control mechanisms.
The challenges to be met include cooperation with humans, uncertainty
in both task and environments, real-time action requirements, and the
use of tools. The challenges cannot be met by merely improving the
software engineering and programming techniques. Rather the systems
need built-in capabilities to deal with these challenges. Looking at
natural intelligent systems, the most promising approach for handling
them is to equip the systems with more powerful cognitive mechanisms.
The potential impact of bringing cognition, control and learning
methods together for robotic manipulation can be enormous. This urge
for such concerted approaches is reflected by a large number of
national and international research initiatives including the DARPA
cognitive systems initiative of the Information Processing Technoloy
Office, various integrated projects funded by the European Community,
the British Foresight program for cognitive systems, huge Japanese
research efforts, to name only a few.
As a result, many researchers all over the world engage in cognitive
system research and there is need for and value in discussion. These
discussions become particularly promising because of the growing
readiness of researchers of different disciplines to talk to each
other.
Early results of such interdisciplinary crossfertilization can already
be observed and we only intend to give a few examples: Cognitive
psychologists have presented empirical evidence for the use of
Bayesian estimation and discovered the cost functions possibly
underlying human motor control. Neuroscientists have shown that
reinforcement learning algorithms can be used to explain the role of
Dopamine in the human basal ganglia as well as the functioning of the
bea brain. Computer scientists and engineers have shown that the
understanding of brain mechanisms can result into realiable learning
algorithms as well as control setups. Insights from artificial
intelligence such as Bayesian networks and the associated reasoning
and learning mechanisms have inspired research in cognitive
psychology, in particular the formation of causal theory in young
children.
These examples suggest that (1)~successful computational mechanisms in
artificial cognitive systems tend to have counterparts with similar
functionality in natural cognitive systems; and (2)~new consolidated
findings about the structure and functional organization of perception
and motion control in natural cognitive systems indicate in a number
of cases much better ways of organizing and specifying computational
tasks in artificial cognitive systems
Prospection in Cognition: The Case for Joint Episodic-Procedural Memory in Cognitive Robotics
No full textProspection lies at the core of cognition: it is the means by which an agent — a person or a cognitive robot — shifts its perspective from immediate sensory experience to anticipate future events, be they the actions of other agents or the outcome of its own actions. Prospection, accomplished by internal simulation, requires mechanisms for both perceptual imagery and motor imagery. While it is known that these two forms of imagery are tightly entwined in the mirror neuron system, we do not yet have an effective model of the mentalizing network which would provide a framework to integrate declarative episodic and procedural memory systems and to combine experiential knowledge with skillful know-how. Such a framework would be founded on joint perceptuo-motor representations. In this paper we examine the case for this form of representation, contrasting sensory-motor theory with ideo-motor theory, and we discuss how such a framework could be realized by joint episodic-procedural memory. We argue that such a representation framework has several advantages for cognitive robotics. Since episodic memory operates by recombining imperfectly recalled past experience, this allows it to simulate new or unexpected events. Furthermore, by virtue of its associative nature, joint episodic-procedural memory allows the internal simulation to be conditioned by current context, semantic memory, and the agent’s value system. Context and semantics constrain the combinatorial explosion of potential perception-action associations and allow effective action selection in the pursuit of goals, while the value system provides the motives that underpin the agent’s autonomy and cognitive development. This joint episodic-procedural memory framework is neutral regarding the final implementation of these episodic and procedural memories, which can be configured sub-symbolically as associative networks or symbolically as content-addressable image databases and databases of motor-control scripts
Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge
No full textOur goal is to enable robots to interpret and execute high-level tasks conveyed using natural language instructions. For example, consider tasking a household robot to, “prepare my breakfast”, “clear the boxes on the table” or “make me a fruit milkshake”. Interpreting such underspecified instructions requires environmental context and background knowledge about how to accomplish complex tasks. Further, the robot’s workspace knowledge may be incomplete: the environment may only be partially-observed or background knowledge may be missing causing a failure in plan synthesis. We introduce a probabilistic model that utilizes background knowledge to infer latent or missing plan constituents based on semantic co-associations learned from noisy textual corpora of task descriptions. The ability to infer missing plan constituents enables information-seeking actions such as visual exploration or dialogue with the human to acquire new knowledge to fill incomplete plans. Results indicate robust plan inference from under-specified instructions in partially-known worlds
Robots Helping Humans: Collaborative Shelf Refilling
No full textThis chapter presents the ergonomic assessment of a typical shelf filling task performed by the store clerk. The proposed methodology is based on a robust design approach, which considers all the main factors that have influence on the ergonomic assessment of a typical refilling operation. The ergonomic assessment is based on two ergonomic indices, one specific for establishing the ergonomically optimal working height for lifting, and one specific for selecting the refilling process modality which minimises the clerks’ effort. The research work has been performed using both virtual simulations and real laboratory experiments. The goal is to provide input to a suitably designed robotic handling unit encapsulating a standard supermarket trolley. The handling unit consists in a suitable SCARA-like arm and two actuated trays, which allow to serve the cases with the products contained in the trolley at an ergonomic height for the clerks, with the aim of reducing refilling-related musculoskeletal disorders and thus improve clerks’ health and wellbeing
Manipulation Planning and Control for Shelf Replenishment
No full textManipulation 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
Towards a cognitive architecture to enable natural language interaction in co-constructive task learning
No full textScheibl M, Richter B, Müller A, Beetz M, Wrede B. Towards a cognitive architecture to enable natural language interaction in co-constructive task learning. In: 2025 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE; 2025: 170-177
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