Object transportation by a human and a mobile manipulator : a dynamical systems approach

In this paper we address the problem of humanrobot joint transportation of large payloads. The human brings to the task knowledge on the goal destination and global path planning. The robot has no prior knowledge of the environment and must autonomously help the human, while simultaneously avoiding static and/or dynamic obstacles that it encounters. For this purpose a dynamic control architecture, formalized as a coupled system of non-linear differential equations, is designed to control the behavior of the mobile manipulator in close loop with the acquired sensorial information. Verbal communication is integrated that allows the robot to communicate its limitations. Results show the robot’s ability to generate stable, smooth and robust behavior in unstructured and dynamic environments. Furthermore, the robot is able to explain the difficulties it encounters and thus contribute to success of the task and to enhance the human-robot physical interaction.FP6-IST2-EU-project JAST (project no 003747)Portuguese Science and Technology Foundation (FCT) and FEDER project COOPDYN (POSI/SRI/38081/2001)

Attractor dynamics approach to joint transportation by autonomous robots: theory, implementation and validation on the factory floor

This paper shows how non-linear attractor dynamics can be used to control teams of two autonomous mobile robots that coordinate their motion in order to transport large payloads in unknown environments, which might change over time and may include narrow passages, corners and sharp U-turns. Each robot generates its collision-free motion online as the sensed information changes. The control architecture for each robot is formalized as a non-linear dynamical system, where by design attractor states, i.e. asymptotically stable states, dominate and evolve over time. Implementation details are provided, and it is further shown that odometry or calibration errors are of no significance. Results demonstrate flexible and stable behavior in different circumstances: when the payload is of different sizes; when the layout of the environment changes from one run to another; when the environment is dynamice.g. following moving targets and avoiding moving obstacles; and when abrupt disturbances challenge team behavior during the execution of the joint transportation task.- This work was supported by FCT-Fundacao para a Ciencia e Tecnologia within the scope of the Project PEst-UID/CEC/00319/2013 and by the Ph.D. Grants SFRH/BD/38885/2007 and SFRH/BPD/71874/2010, as well as funding from FP6-IST2 EU-IP Project JAST (Proj. Nr. 003747). We would like to thank the anonymous reviewers, whose comments have contributed to improve the paper

The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull enough to complete various assembly processes in weakly-structured settings. Moreover, it remains a great challenge to enable operators for teaching robots on-site, while managing the inherent complexity of perception, control, motion planning and reaction to unexpected situations. Motivated by real-world industrial applications, this paper demonstrates the potential of such a paradigm shift in robotics on the industrial case of an e-Bike motor assembly. The paper presents a concept for teaching and programming adaptive robots on-site and demonstrates their potential for the named applications. The framework includes: (i) a method to teach perception systems onsite in a self-supervised manner, (ii) a general representation of object-centric motion skills and force-sensitive assembly skills, both learned from demonstration, (iii) a sequencing approach that exploits a human-designed plan to perform complex tasks, and (iv) a system solution for adapting and optimizing skills online. The aforementioned components are interfaced through a four-layer software architecture that makes our framework a tangible industrial technology. To demonstrate the generality of the proposed framework, we provide, in addition to the motivating e-Bike motor assembly, a further case study on dense box packing for logistics automation

How active perception and attractor dynamics shape perceptual categorization: A computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system