Human Brain Function in Path Planning: a Task Study

Marghi, Yeganeh M.; Towhidkhah, Farzad; Gharibzadeh, Shahriar

doi:10.1007/s12559-016-9443-3

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…This allows for a holistic approach to navigation, the same mechanism drives the decision making process at both levels. This two-stage process complies with the findings of [66], which indicates that human planning relies on a model based prediction mechanism and that the limiting factor in human planning might be the planning horizon. Furthermore in [67,68] they make the same distinction between lower-level motor commands and human path planning.…”

Section: Discussionsupporting

confidence: 82%

Robot navigation as hierarchical active inference

et al. 2021

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Section: Discussionsupporting

confidence: 82%

Robot navigation as hierarchical active inference

et al. 2021

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“…For an artificial agent to demonstrate realistic and purposeful behavior, this agent should possess the abilities to explore, identify, and internalize information from its immediate surroundings. Recently, Marghi and colleagues [30] argued in favor of developing information processing frameworks that focus more on learning and the formation of internal models than direct geometric processing of spatial information for reasoning.…”

Section: Introductionmentioning

confidence: 99%

Information Theoretic Model to Simulate Agent-Signage Interaction for Wayfinding

et al. 2019

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Signage systems are critical for communicating spatial information during wayfinding among a plethora of noise in the environment. A proper signage system can improve wayfinding performance and user experience by reducing the perceived complexity of the environment. However, previous models of sign-based wayfinding do not incorporate realistic noise or quantify the reduction in perceived complexity from the use of signage. Drawing upon concepts from information theory, we propose and validate a new agent-signage interaction model that quantifies available wayfinding information from signs for wayfinding. We conducted two online crowd-sourcing experiments to compute the distribution of a sign's visibility and an agent's decision-making confidence as a function of observation angle and viewing distance. We then validated this model using a virtual reality (VR) experiment with trajectories from human participants. The crowd-sourcing experiments provided a distribution of decision-making entropy (conditioned on visibility) that can be applied to any sign/environment. From the VR experiment, a training dataset of 30 trajectories was used to refine our model, and the remaining test dataset of 10 trajectories was compared with agent behavior using dynamic time warping (DTW) distance. The results revealed a reduction of 38.76% in DTW distance between the average trajectories before and after refinement. Our refined agentsignage interaction model provides realistic predictions of human wayfinding behavior using signs. These findings represent a first step towards modeling human wayfinding behavior in complex real environments in a manner that can incorporate several additional random variables (e.g., environment layout). Keywords Cognitive agent • Information theory • Entropy • Artificial intelligence • Agent-based model • Signage system Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…Further, PMP augments the idea that cognitive motor processes such as action planning shares the same representations with motor execution [ 58 ]. This allows a cognitive agent to reason about and plan its actions in the environment beforehand in a goal-directed fashion [ 37 , 38 , 74 ]. In this sense, PMP framework closely resonates with the embodied simulation hypothesis discussed in the Introduction.…”

Section: The Computational Frameworkmentioning

confidence: 99%

“…In contrast to direct geometric processing of perceptual and spatial information for reasoning and inference, the proposed framework looks at learning and formation of internal models instead . This approach is motivated by evidence from neurosciences related to embodied simulation and prediction [ 37 – 40 ] in particular: Simulation of action : We can activate motor structures of the brain in a way that resembles activity during a normal action but does not cause any overt movement [ 39 , 41 ]; Simulation of perception : Imagining perceiving something is actually similar to perceiving it in reality, only difference being that the perceptual activity is generated by the brain itself rather than by external stimuli [ 42 , 43 ]; Anticipation : There exist associative mechanisms that enable both behavioural and perceptual activity to elicit other perceptual activity in the sensory areas of the brain. Most importantly, a simulated action can elicit perceptual activity that resembles the activity that would have occurred if the action had actually been performed [ 38 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

Bhat

Mohan

2018

Cogn Comput

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From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms.Electronic supplementary materialThe online version of this article (10.1007/s12559-018-9553-1) contains supplementary material, which is available to authorized users.

show abstract

Human Brain Function in Path Planning: a Task Study

Cited by 5 publications

References 34 publications

Robot navigation as hierarchical active inference

Robot navigation as hierarchical active inference

Information Theoretic Model to Simulate Agent-Signage Interaction for Wayfinding

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

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