A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discotinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and posterior parietal cortex can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Cortical Dynamics of Navigation and Steering in Natural Scenes: Motion-Based Object Segmentation, Heading, and Obstacle Avoidance

Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT-/MSTv and MT+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National Geospatial Intelligence Agency (NMA201-01-1-2016

A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016

Human walking behavior: the effect of pedestrian flow and personal space invasions on walking speed and direction

Humans have a natural desire to keep a certain spatial distance to other humans, called personal space (Hall, 1966). If personal space is invaded without consent physiological reactions such as increased heart rate, sweating, and increased blood pressure are triggered (Middlemist et al., 1976). Using a newly developed system called CCB Analyser the walking pa!erns of pedestrians in an Austrian shopping center were recorded. Data included number of people, average speed, speed changes, direction changes, and two different measures for personal space, one being personal space in circles around stationary #ames and the other being personal space for pedestrians integrating the paths ahead. Results show that people walk faster when personal space is invaded, and walking speed and direction are changed to a higher degree at high pedestrian $ow. %ese results show how crowded situations require behavioral changes and offer an important insight into the relationship of human walking behavior and personal space

Action in Mind: Neural Models for Action and Intention Perception

To notice, recognize, and ultimately perceive the others’ actions and to discern the intention behind those observed actions is an essential skill for social communications and improves markedly the chances of survival. Encountering dangerous behavior, for instance, from a person or an animal requires an immediate and suitable reaction. In addition, as social creatures, we need to perceive, interpret, and judge correctly the other individual’s actions as a fundamental skill for our social life. In other words, our survival and success in adaptive social behavior and nonverbal communication depends heavily on our ability to thrive in complex social situations. However, it has been shown that humans spontaneously can decode animacy and social interactions even from strongly impoverished stimuli and this is a fundamental part of human experience that develops early in infancy and is shared with other primates. In addition, it is well established that perceptual and motor representations of actions are tightly coupled and both share common mechanisms. This coupling between action perception and action execution plays a critical role in action understanding as postulated in various studies and they are potentially important for our social cognition. This interaction likely is mediated by action-selective neurons in the superior temporal sulcus (STS), premotor and parietal cortex. STS and TPJ have been identified also as coarse neural substrate for the processing of social interactions stimuli. Despite this localization, the underlying exact neural circuits of this processing remain unclear. The aim of this thesis is to understand the neural mechanisms behind the action perception coupling and to investigate further how human brain perceive different classes of social interactions. To achieve this goal, first we introduce a neural model that provides a unifying account for multiple experiments on the interaction between action execution and action perception. The model reproduces correctly the interactions between action observation and execution in several experiments and provides a link towards electrophysiological detailed models of relevant circuits. This model might thus provide a starting point for the detailed quantitative investigation how motor plans interact with perceptual action representations at the level of single-cell mechanisms. Second we present a simple neural model that reproduces some of the key observations in psychophysical experiments about the perception of animacy and social interactions from stimuli. Even in its simple form the model proves that animacy and social interaction judgments partly might be derived by very elementary operations in hierarchical neural vision systems, without a need of sophisticated or accurate probabilistic inference

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