Neha Das — accepted AI-conference papers

2020

Learning State-Dependent Losses for Inverse Dynamics Learning

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

IROS 2020poster

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 model's parameters, data efficiency is crucial. Given observed data, a key element to how an optimizer updates model par…

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2020

Model-Based Inverse Reinforcement Learning from Visual Demonstrations

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

CoRL 2020

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 dynamics models, developing algorithms that scale to high-dimensional state-spaces and being able to learn from both visu

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