Neha Das

According to our database, Neha Das authored at least 10 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Framework for Learning a Hand Intent Recognition Model from sEMG for FES-Based control.

Neha Das, Satoshi Endo, Hossein Kavianirad, Sandra Hirche

Proceedings of the 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, 2024

2023

Online detection of compensatory strategies in human movement with supervised classification: a pilot study.

Neha Das, Satoshi Endo, Sabrina Patel, Carmen Krewer, Sandra Hirche

Frontiers Neurorobotics, June, 2023

Multimodal Learning of Keypoint Predictive Models for Visual Object Manipulation.

Sarah Bechtle, Neha Das, Franziska Meier

IEEE Trans. Robotics, April, 2023

Time Series Classification for Detecting Parkinson's Disease from Wrist Motions.

Cedric Donié, Neha Das, Satoshi Endo, Sandra Hirche

CoRR, 2023

Assessing Human-Human Kinematics for the Implementation of Robot-Assisted Physical Therapy in Humanoids: A Pilot Study.

Simone Nertinger, Neha Das, Satoshi Endo, Abdeldjallil Naceri, Sandra Hirche, Sami Haddadin

Proceedings of the International Conference on Rehabilitation Robotics, 2023

2022

Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions.

Armin Lederer, Azra Begzadic, Neha Das, Sandra Hirche

CoRR, 2022

2020

Learning Extended Body Schemas from Visual Keypoints for Object Manipulation.

Sarah Bechtle, Neha Das, Franziska Meier

CoRR, 2020

Learning State-Dependent Losses for Inverse Dynamics Learning.

Kristen Morse, Neha Das, Yixin Lin, Austin S. Wang, Akshara Rai, Franziska Meier

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Model-Based Inverse Reinforcement Learning from Visual Demonstrations.

Neha Das, Sarah Bechtle, Todor Davchev, Dinesh Jayaraman, Akshara Rai, Franziska Meier

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations.

Neha Das, Maximilian Karl, Philip Becker-Ehmck, Patrick van der Smagt

CoRR, 2019