Inferring effort-safety trade off in perturbed squat-to-stand task by reward parameter estimation

Name: Inferring effort-safety trade off in perturbed squat-to-stand task by reward parameter estimation
Author: Oztop, Erhan, Babic, J., Ugur, E., Amirshirzad, Negin, Kunavar, T., Arditi, Emir

İsim	Inferring effort-safety trade off in perturbed squat-to-stand task by reward parameter estimation
Yazar	Oztop, Erhan, Babic, J., Ugur, E., Amirshirzad, Negin, Kunavar, T., Arditi, Emir
Basım Tarihi:	2025-02-15
Basım Yeri	- Elsevier
Konu	Experimental behavior analysis, Motor control, Inverse reinforcement learning, Bi-level optimization, Effort safety trade-off
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0952-1976
Kayıt Numarası	cf03efa8-28a1-42d4-a39e-bcdbb11c1fe3
Lokasyon	Computer Science
Tarih	2025-02-15
Örnek Metin	In this study, an inverse reinforcement learning (IRL) method is developed to estimate the parameters of a reward function that is assumed to guide the movement of a biological or artificial agent. The workings of the method is shown on the problem of estimating the effort-safety trade-off of humans during perturbed squat-to-stand motions based on their Center of Mass (COM) trajectories. The proposed method involves data generation by reinforcement learning (RL) and a novel data augmentation mechanism followed by neural network training. After the training, the neural network acts as the reward parameter estimator given the Center of Mass (COM) trajectories as input. The performance of the developed method is assessed through systematic simulation experiments, where it is shown that the parameter estimation made by our method is significantly more accurate than the baseline of an optimized template-based IRL approach. In addition, as a proof of concept, a set of human movement data is analyzed with the developed method. The results revealed that most participants acquired a strategy that ensures low effort expenditure with a safety margin, producing COM trajectories slightly away from the effort-optimal.
DOI	10.1016/j.engappai.2024.109778
Cilt	142

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Inferring effort-safety trade off in perturbed squat-to-stand task by reward parameter estimation

Yazar Oztop, Erhan, Babic, J., Ugur, E., Amirshirzad, Negin, Kunavar, T., Arditi, Emir

Basım Tarihi 2025-02-15

Basım Yeri - Elsevier

Konu Experimental behavior analysis, Motor control, Inverse reinforcement learning, Bi-level optimization, Effort safety trade-off

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0952-1976

Kayıt Numarası cf03efa8-28a1-42d4-a39e-bcdbb11c1fe3

Lokasyon Computer Science

Tarih 2025-02-15

Örnek Metin In this study, an inverse reinforcement learning (IRL) method is developed to estimate the parameters of a reward function that is assumed to guide the movement of a biological or artificial agent. The workings of the method is shown on the problem of estimating the effort-safety trade-off of humans during perturbed squat-to-stand motions based on their Center of Mass (COM) trajectories. The proposed method involves data generation by reinforcement learning (RL) and a novel data augmentation mechanism followed by neural network training. After the training, the neural network acts as the reward parameter estimator given the Center of Mass (COM) trajectories as input. The performance of the developed method is assessed through systematic simulation experiments, where it is shown that the parameter estimation made by our method is significantly more accurate than the baseline of an optimized template-based IRL approach. In addition, as a proof of concept, a set of human movement data is analyzed with the developed method. The results revealed that most participants acquired a strategy that ensures low effort expenditure with a safety margin, producing COM trajectories slightly away from the effort-optimal.

DOI 10.1016/j.engappai.2024.109778

Cilt 142