activity
20172021
most citedDeep Predictive Policy Training using Reinforcement Learning

16 citations · 22 across the 7 of their papers we have counts for

collaborators

12 papers

q-bio.NC20212 cited

Bridging the gap between emotion and joint action

M. M. N. Bieńkiewicz, A. Smykovskyi, T. Olugbade +5

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd.…

cs.LG20212 cited

Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series

Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis +1

Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models.…

cs.CV20212 cited

Graph-based Normalizing Flow for Human Motion Generation and Reconstruction

Wenjie Yin, Hang Yin, Danica Kragic +1

Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics. Challenges remain in these fields for generating…

cs.RO2021

Combining Planning and Learning of Behavior Trees for Robotic Assembly

Jonathan Styrud, Matteo Iovino, Mikael Norrlöf +2

Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well. An increasi…

cs.RO2021

Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms

Ali Ghadirzadeh, Xi Chen, Petra Poklukar +3

Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. A policy trained with expensi…

cs.RO2020

Human-centered collaborative robots with deep reinforcement learning

Ali Ghadirzadeh, Xi Chen, Wenjie Yin +3

We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk o…