Bayesian Human Motion Intentionality Prediction in urban environments

Ferrer, Gonzalo; Sanfeliu, Alberto

doi:10.1016/j.patrec.2013.08.013

Cited by 55 publications

(58 citation statements)

References 15 publications

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“…Gaze was also used by Ivaldi et al (2014a) in a human-robot interaction game with iCub, where the robot (human) was tracking the human (robot) gaze to identify the target object. Ferrer and Sanfeliu (2014) proposed the Bayesian Human Motion Intentionality Prediction algorithm, to geometrically compute the most likely target of the human motion, using Expectation-Maximization and a simple Bayesian classifier. In Wang et al (2012), a method called Intention-Driven Dynamics model, based on Gaussian Process Dynamical Models (GPDM) (Wang et al, 2005), is used to infer the intention of the robot's partner during a ping-pong match, represented by the target of the ball, by analyzing the entire human movement before the human hits the ball.…”

Section: Intention During Human-robot Interactionmentioning

confidence: 99%

Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives

et al. 2017

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This article describes our open-source software for predicting the intention of a user physically interacting with the humanoid robot iCub. Our goal is to allow the robot to infer the intention of the human partner during collaboration, by predicting the future intended trajectory: this capability is critical to design anticipatory behaviors that are crucial in human-robot collaborative scenarios, such as in co-manipulation, cooperative assembly, or transportation. We propose an approach to endow the iCub with basic capabilities of intention recognition, based on Probabilistic Movement Primitives (ProMPs), a versatile method for representing, generalizing, and reproducing complex motor skills. The robot learns a set of motion primitives from several demonstrations, provided by the human via physical interaction. During training, we model the collaborative scenario using human demonstrations. During the reproduction of the collaborative task, we use the acquired knowledge to recognize the intention of the human partner. Using a few early observations of the state of the robot, we can not only infer the intention of the partner but also complete the movement, even if the user breaks the physical interaction with the robot. We evaluate our approach in simulation and on the real iCub. In simulation, the iCub is driven by the user using the Geomagic Touch haptic device. In the real robot experiment, we directly interact with the iCub by grabbing and manually guiding the robot's arm. We realize two experiments on the real robot: one with simple reaching trajectories, and one inspired by collaborative object sorting. The software implementing our approach is open source and available on the GitHub platform. In addition, we provide tutorials and videos.

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Section: Intention During Human-robot Interactionmentioning

confidence: 99%

Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives

et al. 2017

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“…Also, the tracker uses a similar approach for confirmation and elimination of the targets as the developed by [1], but in this case it has been modified to take into account the specificity of the port terminals. Moreover, it has been used the object tracking prediction of [5] and the local coordinates system described in [3]. Although this paper is focused in port environments, the tracker here explained has been also tested for people following in urban environments.…”

Section: Tracking and Filtering Moving Objectsmentioning

confidence: 99%

Low Cost, Robust and Real Time System for Detecting and Tracking Moving Objects to Automate Cargo Handling in Port Terminals

Vaquero

Repiso

Sanfeliu

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract. The presented paper addresses the problem of detecting and tracking moving objects for autonomous cargo handling in port terminals using a perception system which input data is a single layer laser scanner. A computationally low cost and robust Detection and Tracking Moving Objects (DATMO) algorithm is presented to be used in autonomous guided vehicles and autonomous trucks for efficient transportation of cargo in ports. The method first detects moving objects and then tracks them, taking into account that in port terminals the structure of the environment is formed by containers and that the moving objects can be trucks, AGV, cars, straddle carriers and people among others. Two approaches of the DATMO system have been tested, the first one is oriented to detect moving obstacles and focused on tracking and filtering those detections; and the second one is focused on keepking targets when no detections are provided. The system has been evaluated with real data obtained in the CTT port terminal in Hengelo, the Netherlands. Both methods have been tested in the dataset with good results in tracking moving objects.

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“…As proposed in [4], we calculate the most expectable q goal pi for every person on the scene. This calculation is carried out only once, at the initialization of the algorithm (line 4 in Alg.…”

Section: E Vertex Propagationmentioning

confidence: 99%

“…In this work, we apply geometrical based predictors such as the works of [4] and [5] that infer human motion intentions and afterwards predict human motion in a continuous space, according to the Social Force Model (SFM) [6], and the Extended SFM [7]. Works such as [8] and [9] proposed to model people as a summation of a Potential Field (PF), so it is not a novel idea.…”

Section: Introductionmentioning

confidence: 99%

Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments

Ferrer

Sanfeliu

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a novel approach for robot navigation in crowded urban environments where people and objects are moving simultaneously while a robot is navigating. Avoiding moving obstacles at their corresponding precise moment motivates the use of a robotic planner satisfying both dynamic and nonholonomic constraints, also referred as kynodynamic constraints. We present a proactive navigation approach with respect its environment, in the sense that the robot calculates the reaction produced by its actions and provides the minimum impact on nearby pedestrians. As a consequence, the proposed planner integrates seamlessly planning and prediction and calculates a complete motion prediction of the scene for each robot propagation. Making use of the Extended Social Force Model (ESFM) allows an enormous simplification for both the prediction model and the planning system under differential constraints. Simulations and real experiments have been carried out to demonstrate the success of the proactive kinodynamic planner.

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Bayesian Human Motion Intentionality Prediction in urban environments

Abstract: Human motion prediction in indoor and outdoor scenarios is a key issue towards human robot interaction and intelligent robot navigation in general.In the present work, we propose a new human motion intentionality indica-

Cited by 55 publications

References 15 publications

Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives

Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives

Low Cost, Robust and Real Time System for Detecting and Tracking Moving Objects to Automate Cargo Handling in Port Terminals

Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments

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