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In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS 1 , we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.
Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems. In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.
Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation.
The investigation of neuronal accounts of cognition is closely linked to collaboration between behavioral experiments, theory and application and supports the process of moving from pure behaviorist correlation analysis to gaining a real understanding of the underlying mechanisms. Cognition builds upon the individual behavioral history, and the understanding of cognition is based on neuronal principles.
The study of human behavior incorporates in particular interactive, dynamically changing scenarios with multiple human individuals. Both the acquisition of behavioral data of human subjects, the modeling of behavior, as well as the evaluation in interactive scenarios, makes it necessary to generate simulated images of reality. Simulations allow the investigator to precisely control the structure of the environment the subject interacts with. Furthermore, situations that would be too dangerous in the real world (e.g. near-crash driving situations) can be investigated using virtual reality.
By nature, simulated reality frameworks are designed to simulate naturalistic environments. Within these environments, ecologically relevant stimuli embedded in a meaningful and controlled context can be presented. The quality of experimental data acquired within the simulated environment depends not to the last on the degree of immersion of the human subject.
Driving experiments usually attempt to relate observable driver behavior to cognitive inputs. The precise visual (retinal) input of a driver in a driving simulator depends also on the exact position of his head with respect to the screen (Noth et al., 2010). The major meaning of ego motion feedback can be considered as a continuous calibration here.
In a virtual cooperation scenario, consistency matters - if an operator perceives an object at 1 m distance, moving 20 cm towards it should decrease the perceived distance to 80 cm, moving to the side of an object which occludes another one should reveal the latter (Pretto et al., 2009).
The ego-motion feedback mitigates the cues that remind operators of the fact that they are in a virtual and not in the real world. The way the appearance of a virtual object changes due to a lateral head movement is identical to its real counterpart, which means that even relations between real and virtual objects remain (Creem-Regehr et al., 2005; Cutting, 1997).
In this contribution we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environments improving immersion in the context of a human-robot collaborative task and in an interactive driving simulator.
For both cases, we explain how the ego motion feedback leads to a more precise comprehension of the virtual scene and how the aspect of immersion influences the feeling of being “really” inside of the virtual scene and the weakening of the awareness of the border between the real and the virtual world.
The neuronal basis of movement preparation, during which movement parameters such as movement direction are assigned values, is fairly well understood (Georgopoulos, 2000). Motor and premotor cortex as well as portions of the parietal cortex represent movement parameters through the activity of neuronal populations (Bastian et al., 2003; Cisek & Kalaska, 2005).
The parameter representation is of dynamic nature, updated in the course of movement. It adapts to boundary conditions of the motion plan or to environmental changes. Schwartz (2004) was able to decode motor cortical activity in the motor cortex and utilized this knowledge to drive a virtual or robotic end-effector. Thus he proved that the motor cortex is involved in the generation of movement planning. At this level of abstraction we assume that the movement of an end-effector, as well as human walking movement, is represented appropriately by its direction and satisfies other constraints, such as obstacle avoidance or movement coordination.
A neuronal dynamic of movement generates goal-directed movements and satisfies other constraints, such as obstacle avoidance. Movement is generated by choosing low-dimensional, behaviorally relevant state variables. Behavioral goals are represented as attractors of dynamical systems over such behavioral variables (Schöner et al., 1995). The robots trajectory emerges as a solution of these dynamical systems, in which the behavioral variables are stabilized at attractors corresponding to behavioral goals. Constraints are included in a similar manner as repellers. Recently we applied this approach to generate reaching movements for manipulators under obstacle avoidance and orientation con- straints (Iossifidis & Schöner, 2009; Reimann et al., 2010a,b).
We aim to develop an approach to robotic action based on dynamical systems 1
that is quantitatively modeled on human behavior. By varying the intrinsic parameters obtained for different individuals we will be able to implement different personal styles of movement. In this contribution we implement the neuronal dynamics of movement on a humanoid robotic system which generates goal-directed walking movements while avoiding obstacles.
Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple. In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.
We present an architecture based on the Dynamic Field Theory for the problem of scene representation. At the core of this architecture are three-dimensional neural fields linking feature to spatial information. These three-dimensional fields are coupled to lower-dimensional fields that provide both a close link to the sensory surface and a close link to motor behavior. We highlight the updating mechanism of this architecture, both when a single object is selected and followed by the robot's head in smooth pursuit and in multi-item tracking when several items move simultaneously