8 citations · 22 across the 7 of their papers we have counts for
7 papers
Estimating Motor Symptom Presence and Severity in Parkinson's Disease from Wrist Accelerometer Time Series using ROCKET and InceptionTime
Cedric Donié, Neha Das, Satoshi Endo +1
Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatme…
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions
Armin Lederer, Azra Begzadić, Neha Das +1
Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both adherence to safety constraints defined on the system state, as well…
Multi-Modal Learning of Keypoint Predictive Models for Visual Object Manipulation
Sarah Bechtle, Neha Das, Franziska Meier
Humans have impressive generalization capabilities when it comes to manipulating objects and tools in completely novel environments. These capabilities are, at least partially, a r…
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
Neha Das, Sarah Bechtle, Todor Davchev +3
Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem. The key challenges lie in learning good d…
Deep Learning based Uncertainty Decomposition for Real-time Control
Neha Das, Jonas Umlauft, Armin Lederer +2
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty…
Learning State-Dependent Losses for Inverse Dynamics Learning
Kristen Morse, Neha Das, Yixin Lin +3
Being able to quickly adapt to changes in dynamics is paramount in model-based control for object manipulation tasks. In order to influence fast adaptation of the inverse dynamics…
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
Neha Das, Maximilian Karl, Philip Becker-Ehmck +1
Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision maki…