Relaxed Steering towards Oriented Region Goals

Boulic, Ronan

doi:10.1007/978-3-540-89220-5_18

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…This was the reason to introduce the last term, minimizing the integral of Ψ 2 , the difference between body orientation and angular difference to target. The basic idea of this is related to the funneling controller in Boulic (2008), the formulation is however completely different, in our case with a combination of constraints guaranteeing correct final position and orientation and an optimization criterion steering the behavior towards the target. The five weight parameters corresponding to the five base functions of the objective function are a priori unknown.…”

Section: φ(T Z(t) U(t))mentioning

confidence: 99%

From human to humanoid locomotion—an inverse optimal control approach

2009

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The purpose of this paper is to present inverse optimal control as a promising approach to transfer biological motions to robots. Inverse optimal control helps (a) to understand and identify the underlying optimality criteria of biological motions based on measurements, and (b) to establish optimal control models that can be used to control robot motion. The aim of inverse optimal control problems is to determine-for a given dynamic process and an observed solution-the optimization criterion that has produced the solution. Inverse optimal control problems are difficult from a mathematical point of view, since they require to solve a parameter identification problem inside an optimal control problem. We propose a pragmatic new bilevel approach to solve inverse optimal control problems which rests on two pillars: an efficient direct multiple shooting technique to handle optimal control problems, and a state-of-the art derivative free trust region optimization technique to guarantee a match between optimal control problem solution and measurements. In this paper, we apply inverse optimal control to establish a model of human overall locomotion path generation to given target positions and orientations, based on newly collected motion capture data. It is shown how the optimal control model can be implemented on the humanoid robot HRP-2 and thus enable it to autonomously generate natural locomotion paths.

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Section: φ(T Z(t) U(t))mentioning

confidence: 99%

From human to humanoid locomotion—an inverse optimal control approach

2009

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“…An initial approach was introduced by Reynolds [32] as a set of reactive controls. This approach is improved by Boulic [4] by considering oriented targets. Brogan and Johnson [5] introduce real human locomotion behavior in their model by computing the velocity according to acceleration and deceleration rules based on observations of real recordings.…”

Section: Numerical Models Of Goal-directed Locomotionmentioning

confidence: 99%

Kinematic Evaluation of Virtual Walking Trajectories

Cirio

Olivier

Marchal

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The objective of this paper is to evaluate various virtual locomotion conditions by comparing reference with virtual trajectories formed during goal-directed locomotion tasks. Reference trajectories (left) can be recorded through motion capture or be generated through a numerical model of human locomotion. The paper demonstrates the framework (center) over a set of experimental trajectories (right). For the purpose of demonstration, this paper compares frequently used virtual locomotion conditions. Abstract-Virtual walking, a fundamental task in Virtual Reality (VR), is greatly influenced by the locomotion interface being used, by the specificities of input and output devices, and by the way the virtual environment is represented. No matter how virtual walking is controlled, the generation of realistic virtual trajectories is absolutely required for some applications, especially those dedicated to the study of walking behaviors in VR, navigation through virtual places for architecture, rehabilitation and training. Previous studies focused on evaluating the realism of locomotion trajectories have mostly considered the result of the locomotion task (efficiency, accuracy) and its subjective perception (presence, cybersickness). Few focused on the locomotion trajectory itself, but in situation of geometrically constrained task. In this paper, we study the realism of unconstrained trajectories produced during virtual walking by addressing the following question: did the user reach his destination by virtually walking along a trajectory he would have followed in similar real conditions? To this end, we propose a comprehensive evaluation framework consisting on a set of trajectographical criteria and a locomotion model to generate reference trajectories. We consider a simple locomotion task where users walk between two oriented points in space. The travel path is analyzed both geometrically and temporally in comparison to simulated reference trajectories. In addition, we demonstrate the framework over a user study which considered an initial set of common and frequent virtual walking conditions, namely different input devices, output display devices, control laws, and visualization modalities. The study provides insight into the relative contributions of each condition to the overall realism of the resulting virtual trajectories.

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“…The work of [42,43] integrates motor, perceptual, behavioral, and cognitive components within a comprehensive model of pedestrians as individuals. [5] offers the ability of reaching a goal with a prescribed direction by extending the funneling behavior.…”

Section: Related Workmentioning

confidence: 99%

Parallelized egocentric fields for autonomous navigation

et al. 2012

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In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent's local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent's steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations. Abstract In this paper we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent's local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent's steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multi-core systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations.

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Relaxed Steering towards Oriented Region Goals

Cited by 11 publications

References 11 publications

From human to humanoid locomotion—an inverse optimal control approach

From human to humanoid locomotion—an inverse optimal control approach

Kinematic Evaluation of Virtual Walking Trajectories

Parallelized egocentric fields for autonomous navigation

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