Probabilistic human action prediction and wait-sensitive planning for responsive human-robot collaboration

Hawkins, Kelsey P.; Vo, Nam; Bansal, Shray; Bobick, Aaron F.

doi:10.1109/humanoids.2013.7030020

Cited by 72 publications

(50 citation statements)

References 13 publications

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“…Walker et al in [68] predict the behavior of generic agents (e.g., a vehicle) in a visual scene given a large collection of videos. Ziebart et al [78,23] presented a planning based approach.…”

Section: Related Workmentioning

confidence: 99%

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Alahi

Goel

Ramanathan

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2,760

2,594

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Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets . We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model.

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Section: Related Workmentioning

confidence: 99%

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Alahi

Goel

Ramanathan

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2,760

2,594

View full text Add to dashboard Cite

show abstract

“…Walker et al in [38] predict the behavior of generic agents (e.g., a vehicle) in a visual scene given a large collection of videos. Ziebart et al [39,40] presented a planning based approach.…”

Section: Related Workmentioning

confidence: 99%

Learning to Predict Human Behavior in Crowded Scenes

Alahi

Ramanathan

Goel

et al. 2017

Group and Crowd Behavior for Computer Vision

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“…As a result, graphical models have also gained considerable attention in the field of human-robot interaction. In [8], Hawkins and colleagues use a Bayes network to improve the fluency in a joint assembly task. The Bayes network learns to infer the current state of the interaction, as well as task constraints and the anticipated timing of human actions.…”

Section: Related Workmentioning

confidence: 99%

Learning multiple collaborative tasks with a mixture of Interaction Primitives

Ewerton

Neumann

Lioutikov

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box.

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Probabilistic human action prediction and wait-sensitive planning for responsive human-robot collaboration

Cited by 72 publications

References 13 publications

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Learning to Predict Human Behavior in Crowded Scenes

Learning multiple collaborative tasks with a mixture of Interaction Primitives

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