Papers by Wolfram Erlhagen
2018 European Control Conference (ECC), 2018
In this paper we describe a neural field model which explains how a population of cortical neuron... more In this paper we describe a neural field model which explains how a population of cortical neurons may encode in its firing pattern simultaneously the nature and time of sequential stimulus events. From the mathematical point of view, this is obtained my means of a two-dimensional field, where one dimension represents the nature of the event (for example the color of a light signal) and the other represents the elapsed time. Some numerical experiments are reported which were carried out using a computational algorithm for two-dimensional neural field equations. These numerical experiments are described and their results are discussed.
2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2017
If we want robots to engage effectively with humans in service applications or in collaborative w... more If we want robots to engage effectively with humans in service applications or in collaborative work scenarios they have be endowed with the capacity to perceive the passage of time and control the timing of their actions. Here we report result of a robotics experiment in which we test a computational model of action timing based on processing principles of neurodynamics. A key assumption is that elapsed time is encoded in the consistent buildup of persistent population activity representing the memory of sensory or motor events. The stored information can be recalled using a ramp-to-threshold dynamics to guide actions in time. For the experiment we adopt an assembly paradigm from our previous work on natural human-robot interactions. The robot first watches a human executing a sequence of assembly steps. Subsequently, it has to execute the steps from memory in the correct order and in synchrony with an external timing signal. We show that the robot is able to efficiently adapt its motor timing and to store this information in memory using the temporal mismatch between the neural processing of the sensory feedback about executed actions and the external cue.
IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020
This paper proposes a solution for safe navigation of stacker vehicles in workspaces shared with ... more This paper proposes a solution for safe navigation of stacker vehicles in workspaces shared with people, with a focus on the docking manoeuvres for pallet picking and dropping. Behaviours for way-point and wall following are developed following the attractor dynamics approach. Then, these behaviours are orchestrated by state machines (that activate or deactivate them) depending on the specific task. Each of these states also defines different safe areas and maximum travel speeds, which is a requirement for safe operation. Results of real experiments are presented that show the standard operation and its robustness against perturbations (people in the way) and failure detection (missing pallets).
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019
We present a motion controller that generates collision free trajectories for autonomous Tugger v... more We present a motion controller that generates collision free trajectories for autonomous Tugger vehicles operating in dynamic factory environments, where human operators may coexist. The controller is formalized as a dynamic system of path velocity and heading direction, whose vector fields change as sensory information varies. By design the parameters are tuned so that the control variables are close to an attractor of the resultant dynamics most of the time. This contributes to the overall asymptotically stability of the system and makes it robust against perturbations. We present several experiments, in a real factory environment, that highlight different innovative features of the navigation system-flexible and safe solutions for human-aware autonomous navigation in dynamic and cluttered environments. This means, besides generating online collision free trajectories between via points, the system detects the presence of humans, interact with them showing awareness of their presence, and generate adequate motor behavior. Index Terms-Tugger vehicles, flexible and safe autonomous navigation, obstacle avoidance, dynamic environments shared with human operators
Paper presented at the workshop TCV2019: Towards Cognitive Vehicles: perception, learning and dec... more Paper presented at the workshop TCV2019: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bioinspiration helpful? held on November 8, 2019 at 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019).
Abstract: We address the problem of coordinating two non-holonomic mobile robots that move in for... more Abstract: We address the problem of coordinating two non-holonomic mobile robots that move in formation while transporting a long payload. A competitive dynamics is introduced that gradually controls the activation and deactivation of individual behaviors. This process introduces (asymmetrical) hysteresis during behavioral switching. As a result behavioral oscillations, due to noisy information, are eliminated. Results in indoor environments show that if parameter values are chosen within reasonable ranges then, in spite of noise in the robots communication and sensors, the overall robotic system works quite well even in cluttered environments. The robots overt behavior is stable and smooth.
EPJ Web of Conferences, 2021
In this paper, we describe a neural field model which explains how a population of cortical neuro... more In this paper, we describe a neural field model which explains how a population of cortical neurons may encode in its firing pattern simultaneously the nature and time of sequential stimulus events. Moreover, we investigate how noise-induced perturbations may affect the coding process. This is obtained by means of a two-dimensional neural field equation, where one dimension represents the nature of the event (for example, the color of a light signal) and the other represents the moment when the signal has occurred. The additive noise is represented by a Q-Wiener process. Some numerical experiments reported are carried out using a computational algorithm for two-dimensional stochastic neural field equations.
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019
An automatic algorithm to identify Standard Denavit-Hartenberg parameters of serial manipulators ... more An automatic algorithm to identify Standard Denavit-Hartenberg parameters of serial manipulators is proposed. The method is based on geometric operations and dual vector algebra to process and determine the relative transformation matrices, from which it is computed the Standard Denavit-Hartenberg (DH) parameters (ai, αi, di, θi). The algorithm was tested in several serial robotic manipulators with varying kinematic structures and joint types: the KUKA LBR iiwa R800, the Rethink Robotics Sawyer, the ABB IRB 140, the Universal Robots UR3, the KINOVA MICO, and the Omron Cobra 650. For all these robotic manipulators, the proposed algorithm was capable of correctly identifying a set of DH parameters. The algorithm source code as well as the test scenarios are publicly available.
International Journal of Advanced Robotic Systems, 2021
As robots are starting to become part of our daily lives, they must be able to cooperate in a nat... more As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current lit...
Neural Computing and Applications, 2020
Modern manufacturing and assembly environments are characterized by a high variability in the bui... more Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human-robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that applies brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation, 2019
The continuous real-time motor interaction with our environment requires the capacity to measure ... more The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a "resting state" to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions.
In the last decade, the objectives outlined by the needs of personal robotics have led to the ris... more In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analysed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioural and neurological key features and may result...
2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Apr 1, 2017
Useful and efficient human-robot interaction in joint tasks requires the design of a cognitive co... more Useful and efficient human-robot interaction in joint tasks requires the design of a cognitive control architecture that endows robots with crucial cognitive and social capabilities such as intention recognition and complementary action selection. Herein, we present a software framework that eases the design and implementation of Dynamic Neural Field (DNF) cognitive architectures for human-robot joint tasks. We provide a graphical user interface to draw instances of the robot's control architecture. In addition, it allows to simulate, inspect and parametrize them in real-time. The framework eases parameter tuning by allowing changes on-the-fly and by connecting the cognitive architecture with simulated or real robots. Using the case study of an anthropomorphic robot providing assistance to a disabled person during a meal scenario, we illustrate the applicability of the framework.
IEEE Transactions on Cognitive and Developmental Systems, 2020
Many of our sequential activities require that behaviors must be both precisely timed and put in ... more Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This paper presents a neuro-computational model based on the theoretical framework of Dynamic Neural Fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of a learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimension. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions.
Mechanism and Machine Theory, 2018
This paper presents a novel analytic method to uniquely solve inverse kinematics of 7 degrees-of-... more This paper presents a novel analytic method to uniquely solve inverse kinematics of 7 degrees-of-freedom manipulators while avoiding joint limits and singularities. Two auxiliary parameters are introduced to deal with the self-motion manifolds: the global configuration (GC), which specifies the branch of inverse kinematics solutions; and the arm angle (ψ) that parametrizes the elbow redundancy within the specified branch. The relations between the joint angles and the arm angle are derived, in order to map the joint limits and singularities to arm angle values. Then, intervals of feasible arm angles for the specified target pose and global configuration are determined, taking joint limits and singularities into account. A simple metric is proposed to compute the elbow position according to the feasible intervals. When the arm angle is determined, the joint angles can be uniquely calculated from the position-based inverse kinematics algorithm. The presented method does not exhibit the disadvantages inherent to the use of the Jacobian matrix and can be implemented in real-time control systems. This novel algorithm is the first position-based inverse kinematics algorithm to solve both global and local manifolds, using a redundancy resolution strategy to avoid singularities and joint limits.
IFAC Proceedings Volumes, 2004
We propose and demonstrate how attractor dynamics can be used to design and implement a distribut... more We propose and demonstrate how attractor dynamics can be used to design and implement a distributed dynamic control architecture that enables a team of two robots, without force/torque sensors and equipped solely with low-level sensors, to carry a long object and simultaneously avoid obstacles. The explicit required communication between robots is minimal. The robots have no prior knowledge of their environment. Experimental results in indoor environments show that if parameter values are chosen within reasonable ranges then the overall system works quite well even in cluttered environments. The robots' behavior is stable and the generated trajectories are smooth. CopyRigth c 2004IFAC
Physica D: Nonlinear Phenomena, 2016
h i g h l i g h t s • Stable N-bump solutions in a field with N localized inputs are analyzed. • ... more h i g h l i g h t s • Stable N-bump solutions in a field with N localized inputs are analyzed. • Conditions for the shape of the input distribution ensure the existence. • The effect of spatial interactions in a continuous attractor network is discussed. • For a given finite field interval, the maximum number of bumps can be determined. • The results are discussed in terms of a precise spatial memory mechanism.
Neural networks : the official journal of the International Neural Network Society, Jan 19, 2015
There is currently an increasing demand for robots able to acquire the sequential organization of... more There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficie...
IEEE Reviews in Biomedical Engineering, 2015
Stereotactic apparatus to guide surgical devices started being researched in 1908, yet today's ne... more Stereotactic apparatus to guide surgical devices started being researched in 1908, yet today's neurosurgery still rely on stereotactic frames developed almost half a century ago. Robots excel at handling spatial information and thus are an obvious candidate for guiding instrumentation along precisely planned trajectories. In this review, we introduce the concept of stereotaxy and we then describe standard Deep Brain Stimulation (DBS) surgery. Neurosurgeons' expectations and demands about the role of robots as assistive tools are also addressed. We listed and critically reviewed the most successful robotic systems developed specifically or enabled for keyhole transcranial stereotactic neurosurgery. A comprehensive summary details the strengths and drawbacks of each robotic system, emphasising the differences between them. Finally, a critical analysis is made about the listed robotic systems' common and distinct features, and whether they are considered advantages or not. Some essential yet not so obvious characteristics of these systems are also described, along with future perspectives. In the end, all robotic systems follow a very similar and structured workflow despite the technical differences that set them apart. No system unequivocally stands out as an absolute best. Technological progress trend is pointing towards the development of miniaturised, cost-effective solutions with more intuitive interfaces.
Frontiers in Neurorobotics, 2010
How do humans coordinate their intentions, goals and motor behaviors when performing joint action... more How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer's motor system are crucially involved in our ability to understand actions of others', to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human-robot collaboration that exploits this close perceptionaction linkage as a means to achieve more natural and effi cient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fi elds representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the human-robot team has to construct toy objects from their components. The experiments focus on the robot's capacity to anticipate the user's needs and to detect and communicate unexpected events that may occur during joint task execution.
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Papers by Wolfram Erlhagen