Neha Das

Neha Das

Doctoral student

Publications

You can also find my articles on my Google Scholar profile.

Learning Extended Body Schemas from Visual Keypoints for Object Manipulation.

Published in Arxiv, 2021

Humans have impressive generalization capabilities when it comes to manipulating objects and tools in completely novel environments. These capabilities are, at least partially, a result of humans having internal models of their bodies and any grasped object. How to learn such body schemas for robots remains an open problem. In this work, we develop an self-supervised approach that can extend a robot’s kinematic model when grasping an object from visual latent representations.

Cite with: S. Bechtle, N. Das and F. Meier, "Learning Extended Body Schemas from Visual Keypoints for Object Manipulation." 2020 arXiv preprint arXiv:2011.03882. https://arxiv.org/abs/2011.03882

Model-Based Inverse Reinforcement Learning from Visual Demonstrations

Published in CoRL, 2021

In this work, we present a gradient-based inverse reinforcement learning framework that utilizes a pre-trained visual dynamics model to learn cost functions when given only visual human demonstrations. The learned cost functions are then used to reproduce the demonstrated behavior via visual model predictive control.

Cite with: N. Das, S. Bechtle, T. Davchev, D. Jayaraman, A. Rai and F. Meier, "Model-Based Inverse Reinforcement Learning from Visual Demonstrations," 2020 Conference on Robot Learning (CoRL) https://corlconf.github.io/paper_432

Learning State-Dependent Losses for Inverse Dynamics Learning

Published in IROS, 2020

In this work, we propose to apply meta-learning to learn structured, state-dependent loss functions during a meta-training phase. This allows us to quickly adapt the model to changes in dynamics

Cite with: K. Morse, N. Das, Y. Lin, A. S. Wang, A. Rai and F. Meier, "Learning State-Dependent Losses for Inverse Dynamics Learning," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) https://ieeexplore.ieee.org/document/9341701

Beta-DVBF

Published in Arxiv, 2019

In this work, we extend a popular architecture for learning a dynamical system - Deep Variational Bayes Filter - to incorporate high-dimensional image data.

Cite with: N. Das, M. Karl, P. Becker-Ehmck and P. van der Smagt, "Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations." 2019 arXiv preprint arXiv:1911.00756. https://arxiv.org/abs/1911.00756