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Florentin Wörgötter

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33 papers
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33

YNIMG Journal 2025 Journal Article

Touching-untouching patterns organize action representation in the inferior parietal cortex

  • Jennifer Pomp
  • Moritz F. Wurm
  • Rosari N. Selvan
  • Florentin Wörgötter
  • Ricarda I. Schubotz

At an abstract temporospatial level, object-directed actions can be described as sequences of touchings and untouchings of objects, hands, and the ground. These sparse action codes can effectively guide automated systems like robots in recognizing and responding to human actions without the need for object identification. The aim of the current study was to investigate whether the neural processing of actions and their behavioral classification relies on the action categorization derived from the touching-untouching structure. Here we show, using a representational similarity analysis of functional MRI data from two experiments, that action representations in left anterior intraparietal sulcus (aIPS) are particularly associated with this categorization of touching-untouching structures. Within the examined action observation network, only the touching-untouching category model selectively correlated with the representational profile of the left aIPS. The behavioral results showed a significant relation between the touching-untouching structure and the observers' judgments on the similarity of actions with weakly-informative objects. Extending prior research on touchings and untouchings as meaningful anchor points for explicit action segmentation, our findings suggest that touching-untouching sequences serve as an organizing principle in inferior parietal action representation.

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YNIMG Journal 2024 Journal Article

Updating predictions in a complex repertoire of actions and its neural representation

  • Rosari Naveena Selvan
  • Minghao Cheng
  • Sophie Siestrup
  • Falko Mecklenbrauck
  • Benjamin Jainta
  • Jennifer Pomp
  • Anoushiravan Zahedi
  • Minija Tamosiunaite

Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires.

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YNIMG Journal 2021 Journal Article

Touching events predict human action segmentation in brain and behavior

  • Jennifer Pomp
  • Nina Heins
  • Ima Trempler
  • Tomas Kulvicius
  • Minija Tamosiunaite
  • Falko Mecklenbrauck
  • Moritz F. Wurm
  • Florentin Wörgötter

Recognizing the actions of others depends on segmentation into meaningful events. After decades of research in this area, it remains still unclear how humans do this and which brain areas support underlying processes. Here we show that a computer vision-based model of touching and untouching events can predict human behavior in segmenting object manipulation actions with high accuracy. Using this computational model and functional Magnetic Resonance Imaging (fMRI), we pinpoint the neural networks underlying this segmentation behavior during an implicit action observation task. Segmentation was announced by a strong increase of visual activity at touching events followed by the engagement of frontal, hippocampal and insula regions, signaling updating expectation at subsequent untouching events. Brain activity and behavior show that touching-untouching motifs are critical features for identifying the key elements of actions including object manipulations.

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ICRA Conference 2020 Conference Paper

One-Shot Multi-Path Planning for Robotic Applications Using Fully Convolutional Networks

  • Tomas Kulvicius
  • Sebastian Herzog
  • Timo Lüddecke
  • Minija Tamosiunaite
  • Florentin Wörgötter

Path planning is important for robot action execution, since a path or a motion trajectory for a particular action has to be defined first before the action can be executed. Most of the current approaches are iterative methods where the trajectory is generated by predicting the next state based on the current state. Here we propose a novel method by utilising a fully convolutional neural network, which allows generation of complete paths even for several agents with one network prediction iteration. We demonstrate that our method is able to successfully generate optimal or close to optimal paths (less than 10% longer) in more than 99% of the cases for single path predictions in 2D and 3D environments. Furthermore, we show that the network is - without specific training on such cases - able to create (close to) optimal paths in 96% of the cases for two and in 84% of the cases for three simultaneously generated paths.

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AIJ Journal 2019 Journal Article

Distributional semantics of objects in visual scenes in comparison to text

  • Timo Lüddecke
  • Alejandro Agostini
  • Michael Fauth
  • Minija Tamosiunaite
  • Florentin Wörgötter

The distributional hypothesis states that the meaning of a concept is defined through the contexts it occurs in. In practice, often word co-occurrence and proximity are analyzed in text corpora for a given word to obtain a real-valued semantic word vector, which is taken to (at least partially) encode the meaning of this word. Here we transfer this idea from text to images, where pre-assigned labels of other objects or activations of convolutional neural networks serve as context. We propose a simple algorithm that extracts and processes object contexts from an image database and yields semantic vectors for objects. We show empirically that these representations exhibit on par performance with state-of-the-art distributional models over a set of conventional objects. For this we employ well-known word benchmarks in addition to a newly proposed object-centric benchmark.

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IROS Conference 2018 Conference Paper

Prediction of Manipulation Action Classes Using Semantic Spatial Reasoning

  • Fatemeh Ziaeetabar
  • Tomas Kulvicius
  • Minija Tamosiunaite
  • Florentin Wörgötter

Human-robot interaction strongly benefits from fast, predictive action recognition. For us this is relatively easy but difficult for a robot. To address this problem, here we present a novel prediction algorithm for manipulation action classes in video sequences. Manipulations are first represented using the Enriched Semantic Event Chain (ESEC) framework. This creates a temporal sequence of static and dynamic spatial relations between the objects that take part in the manipulation by which an action can be quickly recognized. We measured performance on 32 ideal as well as real manipulations and compared our method also against a state of the art trajectory-based HMM method for action recognition. We observe that manipulations can be correctly predicted after only (on average) 45% of action's total time and that we are almost twice as fast as the HMM-based method. Finally, we demonstrate the advantage of this framework in a simple robot demonstration comparing two different approaches.

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AIJ Journal 2017 Journal Article

Efficient interactive decision-making framework for robotic applications

  • Alejandro Agostini
  • Carme Torras
  • Florentin Wörgötter

The inclusion of robots in our society is imminent, such as service robots. Robots are now capable of reliably manipulating objects in our daily lives but only when combined with artificial intelligence (AI) techniques for planning and decision-making, which allow a machine to determine how a task can be completed successfully. To perform decision making, AI planning methods use a set of planning operators to code the state changes in the environment produced by a robotic action. Given a specific goal, the planner then searches for the best sequence of planning operators, i. e. , the best plan that leads through the state space to satisfy the goal. In principle, planning operators can be hand-coded, but this is impractical for applications that involve many possible state transitions. An alternative is to learn them automatically from experience, which is most efficient when there is a human teacher. In this study, we propose a simple and efficient decision-making framework for this purpose. The robot executes its plan in a step-wise manner and any planning impasse produced by missing operators is resolved online by asking a human teacher for the next action to execute. Based on the observed state transitions, this approach rapidly generates the missing operators by evaluating the relevance of several cause–effect alternatives in parallel using a probability estimate, which compensates for the high uncertainty that is inherent when learning from a small number of samples. We evaluated the validity of our approach in simulated and real environments, where it was benchmarked against previous methods. Humans learn in the same incremental manner, so we consider that our approach may be a better alternative to existing learning paradigms, which require offline learning, a significant amount of previous knowledge, or a large number of samples.

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ICRA Conference 2017 Conference Paper

Semantic analysis of manipulation actions using spatial relations

  • Fatemeh Ziaeetabar
  • Eren Erdal Aksoy
  • Florentin Wörgötter
  • Minija Tamosiunaite

Recognition of human manipulation actions together with the analysis and execution by a robot is an important issue. Also, perception of spatial relationships between objects is central to understanding the meaning of manipulation actions. Here we would like to merge these two notions and analyze manipulation actions using symbolic spatial relations between objects in the scene. Specifically, we define procedures for extraction of symbolic human-readable relations based on Axis Aligned Bounding Box object models and use sequences of those relations for action recognition from image sequences. Our framework is inspired by the so called Semantic Event Chain framework, which analyzes touching and un-touching events of different objects during the manipulation. However, our framework uses fourteen spatial relations instead of two. We show that our relational framework is able to differentiate between more manipulation actions than the original Semantic Event Chains. We quantitatively evaluate the method on the MANIAC dataset containing 120 videos of eight different manipulation actions and obtain 97% classification accuracy which is 12 % more as compared to the original Semantic Event Chains.

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IROS Conference 2016 Conference Paper

Optimal trajectory generation for generalization of discrete movements with boundary conditions

  • Sebastian Herzog
  • Florentin Wörgötter
  • Tomas Kulvicius

Trajectory generation methods play an important role in robotics since they are essential for the execution of actions. In this paper we present a novel trajectory generation method for generalization of accurate movements with boundary conditions. Our approach originates from optimal control theory and is based on a second order dynamic system. We evaluate our method and compare it to state-of-the-art movement generation methods in both simulations and a real robot experiment. We show that the new method is very compact in its representation and can reproduce demonstrated trajectories with zero error. Moreover, it has most of the properties of the state-of-the-art trajectory generation methods such as robustness to perturbations and generalisation to new boundary position and velocity conditions. We believe that, due to these features, our method has great potential for various robotic applications, especially, where high accuracy is required, for example, in industrial and medical robotics.

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IROS Conference 2015 Conference Paper

Semantic parsing of human manipulation activities using on-line learned models for robot imitation

  • Eren Erdal Aksoy
  • Mohamad Javad Aein
  • Minija Tamosiunaite
  • Florentin Wörgötter

Human manipulation activity recognition is an important yet challenging task in robot imitation. In this paper, we introduce, for the first time, a novel method for semantic decomposition and recognition of continuous human manipulation activities by using on-line learned individual manipulation models. Solely based on the spatiotemporal interactions between objects and hands in the scene, the proposed framework can parse not only sequential and concurrent (overlapping) manipulation streams but also basic primitive elements of each detected manipulation. Without requiring any prior object knowledge, the framework can furthermore extract object-like scene entities that are performing the same role in the detected manipulations. The framework was evaluated on our new egocentric activity dataset which contains 120 different samples of 8 single atomic manipulations (e. g. Cutting and Stirring) and 20 long and complex activity demonstrations such as “making a sandwich” and “preparing a breakfast”. We finally show that parsed manipulation actions can be imitated by robots even in various scene contexts with novel objects.

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IROS Conference 2015 Conference Paper

Simultaneously learning at different levels of abstraction

  • Benjamin Quack
  • Florentin Wörgötter
  • Alejandro Agostini

Robotic applications in human environments are usually implemented using a cognitive architecture that integrates techniques of different levels of abstraction, ranging from artificial intelligence techniques for making decisions at a symbolic level to robotic techniques for grounding symbolic actions. In this work we address the problem of simultaneous learning at different levels of abstractions in such an architecture. This problem is important since human environments are highly variable, and many unexpected situations may arise during the execution of a task. The usual approach under this circumstance is to train each level individually to learn how to deal with the new situations. However, this approach is limited since it implies long task interruptions every time a new situation needs to be learned. We propose an architecture where learning takes place simultaneously at all the levels of abstraction. To achieve this, we devise a method that permits higher levels to guide the learning at the levels below for the correct execution of the task. The architecture is instantiated with a logic-based planner and an online planning operator learner, at the highest level, and with online reinforcement learning units that learn action policies for the grounding of the symbolic actions, at the lowest one. A human teacher is involved in the decision-making loop to facilitate learning. The framework is tested in a physically realistic simulation of the Sokoban game.

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IROS Conference 2015 Conference Paper

Using structural bootstrapping for object substitution in robotic executions of human-like manipulation tasks

  • Alejandro Agostini
  • Mohamad Javad Aein
  • Sándor Szedmák
  • Eren Erdal Aksoy
  • Justus H. Piater
  • Florentin Wörgötter

In this work we address the problem of finding replacements of missing objects that are needed for the execution of human-like manipulation tasks. This is a usual problem that is easily solved by humans provided their natural knowledge to find object substitutions: using a knife as a screwdriver or a book as a cutting board. On the other hand, in robotic applications, objects required in the task should be included in advance in the problem definition. If any of these objects is missing from the scenario, the conventional approach is to manually redefine the problem according to the available objects in the scene. In this work we propose an automatic way of finding object substitutions for the execution of manipulation tasks. The approach uses a logic-based planner to generate a plan from a prototypical problem definition and searches for replacements in the scene when some of the objects involved in the plan are missing. This is done by means of a repository of objects and attributes with roles, which is used to identify the affordances of the unknown objects in the scene. Planning actions are grounded using a novel approach that encodes the semantic structure of manipulation actions. The system was evaluated in a KUKA arm platform for the task of preparing a salad with successful results.

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ICRA Conference 2014 Conference Paper

Convexity based object partitioning for robot applications

  • Simon Christoph Stein
  • Florentin Wörgötter
  • Markus Schoeler
  • Jeremie Papon
  • Tomas Kulvicius

The idea that connected convex surfaces, separated by concave boundaries, play an important role for the perception of objects and their decomposition into parts has been discussed for a long time. Based on this idea, we present a new bottom-up approach for the segmentation of 3D point clouds into object parts. The algorithm approximates a scene using an adjacency-graph of spatially connected surface patches. Edges in the graph are then classified as either convex or concave using a novel, strictly local criterion. Region growing is employed to identify locally convex connected subgraphs, which represent the object parts. We show quantitatively that our algorithm, although conceptually easy to graph and fast to compute, produces results that are comparable to far more complex state-of-the-art methods which use classification, learning and model fitting. This suggests that convexity/concavity is a powerful feature for object partitioning using 3D data. Furthermore we demonstrate that for many objects a natural decomposition into “handle and body” emerges when employing our method. We exploit this property in a robotic application enabling a robot to automatically grasp objects by their handles.

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EAAI Journal 2014 Journal Article

Learning weakly correlated cause–effects for gardening with a cognitive system

  • Alejandro Agostini
  • Carme Torras
  • Florentin Wörgötter

We propose a cognitive system that combines artificial intelligence techniques for planning and learning to execute tasks involving delayed and variable correlations between the actions executed and their expected effects. The system is applied to the task of controlling the growth of plants, where the evolution of the plant attributes strongly depends on different events taking place in the temporally distant past history of the plant. The main problem to tackle is how to efficiently detect these past events. This is very challenging since the inclusion of time could make the dimensionality of the search space extremely large and the collected training instances may only provide very limited information about the relevant combinations of events. To address this problem we propose a learning method that progressively identifies those events that are more likely to produce a sequence of changes under a plant treatment. Since the number of experiences is very limited compared to the size of the event space, we use a probabilistic estimate that takes into account the lack of experience to prevent biased estimations. Planning operators are generated from most accurately predicted sequences of changes. Planning and learning are integrated in a decision-making framework that operates without task interruptions by allowing a human gardener to instruct the treatments when the knowledge acquired so far is not enough to make a decision.

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IROS Conference 2014 Conference Paper

Using haptics to extract object shape from rotational manipulations

  • Claudius Strub
  • Florentin Wörgötter
  • Helge J. Ritter
  • Yulia Sandamirskaya

Increasingly widespread available haptic sensors mounted on articulated hands offer new sensory channels that can complement shape extraction from vision to enable a more robust handling of objects in cases when vision is restricted or even unavailable. However, to estimate object shape from haptic interaction data is a difficult challenge due to the complexity of the contact interaction between the movable object and sensor surfaces, leading to a coupled estimation problem of shape and object pose. While for vision efficient solutions to the underlying SLAM problem are known, the available information is much sparser in the tactile case, posing great difficulties for a straightforward adoption of standard SLAM algorithms. In the present paper, we thus explore whether a biologically inspired model based on dynamic neural fields can offer a route towards a practical algorithm for tactile SLAM. Our study is focused on a restricted scenario where a two-fingered robot hand manipulates an n-gon with a fixed rotational axis. We demonstrate that our model can accumulate shape information from reasonably short interaction sequences and autonomously build a representation despite significant ambiguity of the tactile data due to the rotational periodicity of the object. We conclude that the presented framework may be a suitable basis to solve the tactile SLAM problem also in more general settings which will be the focus of subsequent work.

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ICRA Conference 2013 Conference Paper

Adaptive neural oscillators with synaptic plasticity for locomotion control of a snake-like robot with screw-drive mechanism

  • Timo Nachstedt
  • Florentin Wörgötter
  • Poramate Manoonpong
  • Ryo Ariizumi
  • Yuichi Ambe
  • Fumitoshi Matsuno

Central pattern generators (CPGs) play a crucial role for animal locomotion control. They can be entrained by sensory feedback to induce proper rhythmic patterns and even store the entrained patterns through connection weights. Inspired by this biological finding, we use four adaptive neural oscillators with synaptic plasticity as CPGs for locomotion control of our real snake-like robot with screw-drive mechanism. Each oscillator consists of only three neurons and uses adaptive mechanisms based on frequency adaptation and Hebbian-type learning rules. It autonomously generates proper periodic patterns for the robot locomotion and can be entrained by sensory feedback to memorize the patterns. The adaptive CPG system in conjunction with a simple control strategy enables the robot to perform self-tuning behavior which is robust against short-time perturbations. The generated behavior is also energy efficient. In addition, the robot can also cope with corners as well as move through a complex environment with obstacles.

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IROS Conference 2013 Conference Paper

Point cloud video object segmentation using a persistent supervoxel world-model

  • Jeremie Papon
  • Tomas Kulvicius
  • Eren Erdal Aksoy
  • Florentin Wörgötter

Robust visual tracking is an essential precursor to understanding and replicating human actions in robotic systems. In order to accurately evaluate the semantic meaning of a sequence of video frames, or to replicate an action contained therein, one must be able to coherently track and segment all observed agents and objects. This work proposes a novel online point cloud based algorithm which simultaneously tracks 6DoF pose and determines spatial extent of all entities in indoor scenarios. This is accomplished using a persistent supervoxel world-model which is updated, rather than replaced, as new frames of data arrive. Maintenance of a world model enables general object permanence, permitting successful tracking through full occlusions. Object models are tracked using a bank of independent adaptive particle filters which use a supervoxel observation model to give rough estimates of object state. These are united using a novel multi-model RANSAC-like approach, which seeks to minimize a global energy function associating world-model supervoxels to predicted states. We present results on a standard robotic assembly benchmark for two application scenarios — human trajectory imitation and semantic action understanding — demonstrating the usefulness of the tracking in intelligent robotic systems.

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IROS Conference 2013 Conference Paper

Stability analysis of a hexapod robot driven by distributed nonlinear oscillators with a phase modulation mechanism

  • Yuichi Ambe
  • Timo Nachstedt
  • Poramate Manoonpong
  • Florentin Wörgötter
  • Shinya Aoi
  • Fumitoshi Matsuno

In this paper, we investigated the dynamics of a hexapod robot model whose legs are driven by nonlinear oscillators with a phase modulation mechanism including phase resetting and inhibition. This mechanism changes the oscillation period of the oscillator depending solely on the timing of the foot's contact. This strategy is based on observation of animals. The performance of the controller is evaluated using a physical simulation environment. Our simulation results show that the robot produces some stable gaits depending on the locomotion speed due to the phase modulation mechanism, which are simillar to the gaits of insects.

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IROS Conference 2013 Conference Paper

Toward a library of manipulation actions based on semantic object-action relations

  • Mohamad Javad Aein
  • Eren Erdal Aksoy
  • Minija Tamosiunaite
  • Jeremie Papon
  • Ales Ude
  • Florentin Wörgötter

The goal of this study is to provide an architecture for a generic definition of robot manipulation actions. We emphasize that the representation of actions presented here is “procedural”. Thus, we will define the structural elements of our action representations as execution protocols. To achieve this, manipulations are defined using three levels. The toplevel defines objects, their relations and the actions in an abstract and symbolic way. A mid-level sequencer, with which the action primitives are chained, is used to structure the actual action execution, which is performed via the bottom level. This (lowest) level collects data from sensors and communicates with the control system of the robot. This method enables robot manipulators to execute the same action in different situations i. e. on different objects with different positions and orientations. In addition, two methods of detecting action failure are provided which are necessary to handle faults in system. To demonstrate the effectiveness of the proposed framework, several different actions are performed on our robotic setup and results are shown. This way we are creating a library of human-like robot actions, which can be used by higher-level task planners to execute more complex tasks.

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IROS Conference 2012 Conference Paper

Biologically inspired reactive climbing behavior of hexapod robots

  • Dennis Goldschmidt
  • Frank Hesse
  • Florentin Wörgötter
  • Poramate Manoonpong

Insects, e. g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i. e. , body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e. g. , ∼ 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.

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IROS Conference 2012 Conference Paper

Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot

  • Guanjiao Ren
  • Weihai Chen
  • Christoph Kolodziejski
  • Florentin Wörgötter
  • Sakyasingha Dasgupta
  • Poramate Manoonpong

In chaos control, an originally chaotic system is modified so that periodic dynamics arise. One application of this is to use the periodic dynamics of a single chaotic system as walking patterns in legged robots. In our previous work we applied such a controlled chaotic system as a central pattern generator (CPG) to generate different gait patterns of our hexapod robot AMOSII. However, if one or more legs break, its control fails. Specifically, in the scenario presented here, its movement permanently deviates from a desired trajectory. This is in contrast to the movement of real insects as they can compensate for body damages, for instance, by adjusting the remaining legs' frequency. To achieve this for our hexapod robot, we extend the system from one chaotic system serving as a single CPG to multiple chaotic systems, performing as multiple CPGs. Without damage, the chaotic systems synchronize and their dynamics is identical (similar to a single CPG). With damage, they can lose synchronization leading to independent dynamics. In both simulations and real experiments, we can tune the oscillation frequency of every CPG manually so that the controller can indeed compensate for leg damage. In comparison to the trajectory of the robot controlled by only a single CPG, the trajectory produced by multiple chaotic CPG controllers resembles the original trajectory by far better. Thus, multiple chaotic systems that synchronize for normal behavior but can stay desynchronized in other circumstances are an effective way to control complex behaviors where, for instance, different body parts have to do independent movements like after leg damage.

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ICRA Conference 2011 Conference Paper

Accurate position and velocity control for trajectories based on dynamic movement primitives

  • KeJun Ning
  • Tomas Kulvicius
  • Minija Tamosiunaite
  • Florentin Wörgötter

This paper presents a novel method for trajectory generation based on dynamic movement primitives (DMPs) treated from a control theoretical perspective. We extended the key ideas from the original DMP formalism by introducing a velocity convergence mechanism in the reformulated system. Theoretical proof is given to guarantee its validity. The new method can deal with complex paths as a whole. Based on this, we can generate smooth trajectories with automatically generated transition zones, satisfy position- and velocity boundary conditions at start and endpoint with high precision, and support multiple via-point applications. Theoretic proof of this method and experiments are presented.

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ICRA Conference 2011 Conference Paper

Lane shape estimation using a Partitioned Particle filter for autonomous driving

  • Guoliang Liu
  • Florentin Wörgötter
  • Irene Markelic

This paper presents a probabilistic algorithm for lane shape estimation in an urban environment which is important for example for driver assistance systems and autonomous driving. For the first time, we bring together the so-called Partitioned Particle filter, an improvement of the traditional Particle filter, and the linear-parabolic lane model which alleviates many shortcomings of traditional lane models. The former improves the traditional Particle filter by subdividing the whole state space of particles into several subspaces and estimating those subspaces in a hierarchical structure, such that the number of particles for each subspace is flexible and the robustness of the whole system is increased. Furthermore, we introduce a new statistical observation model, an important part of the Particle filter, where we use multi-kernel density to model the probability distribution of lane parameters. Our observation model considers not only color and position information as image cues, but also the image gradient. Our experimental results illustrate the robustness and efficiency of our algorithm even when confronted with challenging scenes.

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ICRA Conference 2011 Conference Paper

Modified dynamic movement primitives for joining movement sequences

  • Tomas Kulvicius
  • KeJun Ning
  • Minija Tamosiunaite
  • Florentin Wörgötter

The generation of complex movement patterns, in particular in cases where one needs to smoothly and accurately join trajectories, is still a difficult problem in robotics. This paper presents a novel approach for joining of several dynamic movement primitives (DMPs) based on a modification of the original formulation for DMPs. The new method produces smooth and natural transitions in position as well as velocity space. The properties of the method are demonstrated by applying it to simulated handwriting generation implemented on a robot, where an adaptive algorithm is used to learn trajectories from human demonstration. These results demonstrate that the new method is a feasible alternative for trajectory learning and generation and its accuracy and modular character has potential for various robotics applications.

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ICRA Conference 2010 Conference Paper

Categorizing object-action relations from semantic scene graphs

  • Eren Erdal Aksoy
  • Alexey Abramov
  • Florentin Wörgötter
  • Babette Dellen

In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. Semantic scene graphs are extracted from image sequences and used to find the characteristic main graphs of the action sequence via an exact graph-matching technique, thus providing an event table of the action scene, which allows extracting object-action relations. The method is applied to several artificial and real action scenes containing limited context. The central novelty of this approach is that it is model free and needs a priori representation neither for objects nor actions. Essentially actions are recognized without requiring prior object knowledge and objects are categorized solely based on their exhibited role within an action sequence. Thus, this approach is grounded in the affordance principle, which has recently attracted much attention in robotics and provides a way forward for trial and error learning of object-action relations through repeated experimentation. It may therefore be useful for recognition and categorization tasks for example in imitation learning in developmental and cognitive robotics.

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IROS Conference 2009 Conference Paper

A DOF state controllable & driving shared solution for building a hyper-redundant chain robot

  • KeJun Ning
  • Florentin Wörgötter

This paper puts forward a novel design solution for building a 3D hyper-redundant chain robot (HRCR) system, which consists of linked, identical modules and one base module. All the joints of this HRCR are passive and state controllable, and share common inputs introduced by wire-driven control, no matter how many degrees of freedom (DOF) are implemented using different numbers of modules. The prototype developed here, named 3D-Trunk, is used as a proof of concept. We will present here its concept, mechanical and embedded controller design and the implementation.

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NeurIPS Conference 2008 Conference Paper

On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor

  • Christoph Kolodziejski
  • Bernd Porr
  • Minija Tamosiunaite
  • Florentin Wörgötter

In this theoretical contribution we provide mathematical proof that two of the most important classes of network learning - correlation-based differential Hebbian learning and reward-based temporal difference learning - are asymptotically equivalent when timing the learning with a local modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation based perspective that is more closely related to the biophysics of neurons.

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NeurIPS Conference 2005 Conference Paper

Fast biped walking with a reflexive controller and real-time policy searching

  • Tao Geng
  • Bernd Porr
  • Florentin Wörgötter

In this paper, we present our design and experiments of a planar biped robot ("RunBot") under pure reflexive neuronal control. The goal of this study is to combine neuronal mechanisms with biomechanics to obtain very fast speed and the on-line learning of circuit parameters. Our controller is built with biologically inspired sensor- and motor-neuron models, including local reflexes and not employing any kind of position or trajectory-tracking control algorithm. Instead, this reflexive controller allows RunBot to exploit its own natural dynamics during critical stages of its walking gait cycle. To our knowledge, this is the first time that dynamic biped walking is achieved using only a pure reflexive controller. In addition, this structure allows using a policy gradient reinforcement learning algorithm to tune the parameters of the reflexive controller in real-time during walking. This way RunBot can reach a relative speed of 3. 5 leg-lengths per second after a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking. In addition, the stability domain of stable walking is quite large supporting this design strategy.

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IROS Conference 2005 Conference Paper

Self-stabilized biped walking under control of a novel reflexive network

  • Tao Geng
  • Bernd Porr
  • Florentin Wörgötter

Biologically inspired reflexive controllers have been implemented on various walking robots. However, due to the natural instability of biped walking, up to date, there has not existed a biped robot that depends exclusively on reflexive controllers for its dynamically stable walking control. In this paper, we present our design and experiments of a planar biped robot under control of a pure reflexive controller that includes only local extensor and flexor reflexes (no any other reflexes for explicit stability control). The reflexive controller is built with biologically inspired stretch receptors and model neurons. It requires fewer phasic feedbacks than those reflexive controllers of multilegged robots, and does not employ any kind of position or velocity control algorithm even on its low level. Instead, the approximate property of this reflexive controller has allowed our biped robot to substantially exploit its own passive dynamics in some stages of its walking gait cycle. Due to the interaction of the reflexive controller and the properly designed mechanics of the robot, the biped robot works as a closely coupled neuromechanical system, and demonstrates self-stabilizing property in the experiments of slightly perturbed walking, shallow slope walking, and various speed walking. Moreover, our biped robot can walk stably at a relatively high speed (nearly three leg-lengths per second). We know of no other biped robots that could attain such a high relative speed.

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NeurIPS Conference 2005 Conference Paper

Temporally changing synaptic plasticity

  • Minija Tamosiunaite
  • Bernd Porr
  • Florentin Wörgötter

Recent experimental results suggest that dendritic and back-propagating spikes can influence synaptic plasticity in different ways [1]. In this study we investigate how these signals could temporally interact at dendrites leading to changing plasticity properties at local synapse clusters. Similar to a previous study [2], we employ a differential Hebbian plasticity rule to emulate spike-timing dependent plasticity. We use dendritic (D-) and back-propagating (BP-) spikes as post-synaptic signals in the learning rule and investigate how their interaction will influence plasticity. We will analyze a situation where synapse plasticity characteristics change in the course of time, depending on the type of post-synaptic activity momentarily elicited. Starting with weak synapses, which only elicit local D-spikes, a slow, unspecific growth process is induced. As soon as the soma begins to spike this process is replaced by fast synaptic changes as the consequence of the much stronger and sharper BP-spike, which now dominates the plasticity rule. This way a winner-take-all-mechanism emerges in a two-stage process, enhancing the best-correlated inputs. These results suggest that synaptic plasticity is a temporal changing process by which the computational properties of dendrites or complete neurons can be substantially augmented.

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NeurIPS Conference 2003 Conference Paper

Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics

  • Bernd Porr
  • Ausra Saudargiene
  • Florentin Wörgötter

Spike timing plasticity (STDP) is a special form of synaptic plasticity where the relative timing of post- and presynaptic activity determines the change of the synaptic weight. On the postsynaptic side, active back- propagating spikes in dendrites seem to play a crucial role in the induc- tion of spike timing dependent plasticity. We argue that postsynaptically the temporal change of the membrane potential determines the weight change. Coming from the presynaptic side induction of STDP is closely related to the activation of NMDA channels. Therefore, we will calculate analytically the change of the synaptic weight by correlating the deriva- tive of the membrane potential with the activity of the NMDA channel. Thus, for this calculation we utilise biophysical variables of the physi- ological cell. The final result shows a weight change curve which con- forms with measurements from biology. The positive part of the weight change curve is determined by the NMDA activation. The negative part of the weight change curve is determined by the membrane potential change. Therefore, the weight change curve should change its shape de- pending on the distance from the soma of the postsynaptic cell. We find temporally asymmetric weight change close to the soma and temporally symmetric weight change in the distal dendrite.

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NeurIPS Conference 2003 Conference Paper

Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina

  • Matthias Hennig
  • Florentin Wörgötter

Even under perfect fixation the human eye is under steady motion (tremor, microsaccades, slow drift). The “dynamic” theory of vi- sion [1, 2] states that eye-movements can improve hyperacuity. Accord- ing to this theory, eye movements are thought to create variable spatial excitation patterns on the photoreceptor grid, which will allow for better spatiotemporal summation at later stages. We reexamine this theory us- ing a realistic model of the vertebrate retina by comparing responses of a resting and a moving eye. The performance of simulated ganglion cells in a hyperacuity task is evaluated by ideal observer analysis. We find that in the central retina eye-micromovements have no effect on the perfor- mance. Here optical blurring limits vernier acuity. In the retinal periph- ery however, eye-micromovements clearly improve performance. Based on ROC analysis, our predictions are quantitatively testable in electro- physiological and psychophysical experiments.

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NeurIPS Conference 2002 Conference Paper

Learning a Forward Model of a Reflex

  • Bernd Porr
  • Florentin Wörgötter

We develop a systems theoretical treatment of a behavioural system that interacts with its environment in a closed loop situation such that its mo- tor actions influence its sensor inputs. The simplest form of a feedback is a reflex. Reflexes occur always “too late”; i. e. , only after a (unpleas- ant, painful, dangerous) reflex-eliciting sensor event has occurred. This defines an objective problem which can be solved if another sensor input exists which can predict the primary reflex and can generate an earlier reaction. In contrast to previous approaches, our linear learning algo- rithm allows for an analytical proof that this system learns to apply feed- forward control with the result that slow feedback loops are replaced by their equivalent feed-forward controller creating a forward model. In other words, learning turns the reactive system into a pro-active system. By means of a robot implementation we demonstrate the applicability of the theoretical results which can be used in a variety of different areas in physics and engineering.

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