activity
20192021
most citedMonte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

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.RO2021

Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitives

Antonis Sidiropoulos, Yiannis Karayiannidis, Zoe Doulgeri

In this work, the problem of human-robot collaborative object transfer to unknown target poses is addressed. The desired pattern of the end-effector pose trajectory to a known targ…

cs.RO2021

Interpretability in Contact-Rich Manipulation via Kinodynamic Images

Ioanna Mitsioni, Joonatan Mänttäri, Yiannis Karayiannidis +2

Deep Neural Networks (NNs) have been widely utilized in contact-rich manipulation tasks to model the complicated contact dynamics. However, NN-based models are often difficult to d…

cs.RO2020

Modelling and Learning Dynamics for Robotic Food-Cutting

Ioanna Mitsioni, Yiannis Karayiannidis, Danica Kragic

Data-driven approaches for modelling contact-rich tasks address many of the difficulties that analytical models bear. For real-world scenarios, the hardware capabilities constrain…

cs.RO2019

Data-Driven Model Predictive Control for Food-Cutting

Ioanna Mitsioni, Yiannis Karayiannidis, Johannes A. Stork +1

Modelling of contact-rich tasks is challenging and cannot be entirely solved using classical control approaches due to the difficulty of constructing an analytic description of the…