Analysis of an adaptive strategy for equitably approaching and joining human interactions

Narayanan, Vishnu K.; Spalanzani, Anne; Luo, Ren C.; Babel, Marie

doi:10.1109/roman.2016.7745153

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…6. Several previous researchers used this formation for people interaction similarly to us [3], [59], [60]. Also, this formation is sometimes generalized for more than three people as an O-shape formation.…”

Section: B Consider the Robot Task In Proxemicsmentioning

confidence: 99%

Real-Life Experiment Metrics for Evaluating Human-Robot Collaborative Navigation Tasks

Repiso,

Garrell,

Sanfeliu

2023

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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As robots move from laboratories and industries to the real world, they must develop new abilities to collaborate with humans in various aspects, including human-robot collaborative navigation (HRCN) tasks. Then, it is required to develop general methodologies to evaluate these robots' behaviors. These methodologies should incorporate objective and subjective measurements. Objective measurements for evaluating a robot's behavior while navigating with others can be accomplished using social distances in conjunction with task characteristics, people-robot relationships, and physical space. Additionally, the objective evaluation of the task must consider human behavior, which is influenced by changes and the structure of their environment. Subjective evaluations of robot's behaviors can be conducted using surveys that address various aspects of robot usability. This includes people's perceptions of their interaction during their collaborative task with the robot, focusing on aspects such as sociability, comfort, and taskintelligence. Moreover, the communicative interaction between the agents (people and robots) involved in the collaborative task should also be evaluated. Therefore, this paper presents a comprehensive methodology for objectively and subjectively evaluating HRCN tasks.

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Section: B Consider the Robot Task In Proxemicsmentioning

confidence: 99%

Real-Life Experiment Metrics for Evaluating Human-Robot Collaborative Navigation Tasks

Repiso,

Garrell,

Sanfeliu

2023

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

View full text Add to dashboard Cite

show abstract

“…It is expected that the group and the target person will form a triangle formation. Some previous works use these formation that seems to be natural for humans [58]. To achieve this formation, first, the robot uses the cosine theorem, Eq.…”

Section: Stage 2: Proactive Configuration To Interact With Both Peoplementioning

confidence: 99%

Adaptive Side-by-Side Social Robot Navigation to Approach and Interact with People

Repiso

Garrell

Sanfeliu

2019

Int J of Soc Robotics

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This paper presents a new framework for how autonomous social robots approach and accompany people in urban environments. The method discussed allows the robot to accompany a person and approach to other one, by adapting its own navigation in anticipation of future interactions with other people or contact with static obstacles. The contributions of the paper are manifold: firstly, we extended the Social Force model and the Anticipative Kinodynamic Planner [1] to the case of an adaptive side-by-side navigation; secondly, we enhance side-by-side navigation with an approaching task and a final positioning that allows the robot to interact with both people; and finally, we use findings from experiments of real-life observations of people walking in pairs to define the parameters of the human-robot interaction in our case of adaptive sideby-side. The method was validated by a large set of simulations; we also conducted real-life experiments with our robot, Tibi, to validate the framework described for the interaction process. In addition, we carried out various surveys and user studies to indicate the social acceptability of the robots performance of the accompanying, approaching and positioning tasks.

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“…A review on the main social-aware frameworks for navigation is presented [21]. Narayanan et al presented an adaptive strategy for the approach phase in human-robot interaction [22]. A human intent-aware strategy for safe and efficient robotic crowd navigation were reported by Park et al [23].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…It is clear from the presented works that a collaborative approach between the robot and humans is critical for safe and efficient navigation of crowds; furthermore, intent conveying and proxemics-aware robots that can deal with social cues, both as receivers and transmitters, are of prime importance [12,14,16,18,[21][22][23].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization

Seriani

Marcini

Caruso

et al. 2021

J Intell Robot Syst

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Crowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment; the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particular, perception is based on a raycasting model which is tailored on the ANN. An in-depth analysis of a number of parameters of the environment and the robot is performed and a comparative analysis is presented; finally, results of the performance of the controller trained with NEAT are compared to those of a human driver who takes over the controller itself. Results show that the intelligent controller is able to perform on par with the human, within the simulated environment.

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Analysis of an adaptive strategy for equitably approaching and joining human interactions

Cited by 4 publications

References 23 publications

Real-Life Experiment Metrics for Evaluating Human-Robot Collaborative Navigation Tasks

Real-Life Experiment Metrics for Evaluating Human-Robot Collaborative Navigation Tasks

Adaptive Side-by-Side Social Robot Navigation to Approach and Interact with People

Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization

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