Add to ORKGIn this extended abstract we present a novel dataset for benchmarking motion prediction algorithms. We describe our approach to data collection which generates diverse and accurate human motion in a controlled weakly-scripted setup. We also give insights for building a universal benchmark for motion prediction.ILIA
International audienceHuman behavior prediction is an interdisciplinary research direction, involvin...
Collaboration between humans and robots is becoming an increasingly commonoccurrence in both industr...
In the present work, we propose and validate a complete probabilistic framework for human motion pre...
With growing numbers of intelligent autonomous systems in human environments, the ability of such sy...
The ability to accurately predict human motion is imperative for any human-robot interaction applica...
Understanding human behavior is a key skill for intelligent systems that share physical and emotiona...
In this paper, we present a novel method to predict human motion, seeking to combine the advantages ...
The capacity of a system to automatically analyze and predict the performance of a human in a partic...
This letter presents a framework for recognition and prediction of ongoing human motions. The predic...
In recent years, motion capture systems have emerged, allowing for a broad and ever-expanding range ...
This research project develops a new deep neural network model for real-time human movement predicti...
Thesis: Ph. D. in Autonomous Systems, Massachusetts Institute of Technology, Department of Aeronauti...
In this paper an optimization-based hybrid dynamic motion prediction method is presented. The method...
We treat the problem of movement prediction as a classification task. We assume the existence of a (...
Over the years, the separate fields of motion planning, mapping, and human trajectory prediction hav...
International audienceHuman behavior prediction is an interdisciplinary research direction, involvin...
Collaboration between humans and robots is becoming an increasingly commonoccurrence in both industr...
In the present work, we propose and validate a complete probabilistic framework for human motion pre...
With growing numbers of intelligent autonomous systems in human environments, the ability of such sy...
The ability to accurately predict human motion is imperative for any human-robot interaction applica...
Understanding human behavior is a key skill for intelligent systems that share physical and emotiona...
In this paper, we present a novel method to predict human motion, seeking to combine the advantages ...
The capacity of a system to automatically analyze and predict the performance of a human in a partic...
This letter presents a framework for recognition and prediction of ongoing human motions. The predic...
In recent years, motion capture systems have emerged, allowing for a broad and ever-expanding range ...
This research project develops a new deep neural network model for real-time human movement predicti...
Thesis: Ph. D. in Autonomous Systems, Massachusetts Institute of Technology, Department of Aeronauti...
In this paper an optimization-based hybrid dynamic motion prediction method is presented. The method...
We treat the problem of movement prediction as a classification task. We assume the existence of a (...
Over the years, the separate fields of motion planning, mapping, and human trajectory prediction hav...
International audienceHuman behavior prediction is an interdisciplinary research direction, involvin...
Collaboration between humans and robots is becoming an increasingly commonoccurrence in both industr...
In the present work, we propose and validate a complete probabilistic framework for human motion pre...