3 citations · 4 across the 7 of their papers we have counts for
7 papers
Steering Multirobot Behavior via Closed-Loop Affine Activation Editing
Satyajeet Das, Darren Chiu, Shashank Hegde +1
Real-world robots need to adapt their behavior beyond the envelope of their pre-trained policy. Policy finetuning or retraining are options, but they risk catastrophic forgetting,…
WARPD: World model Assisted Reactive Policy Diffusion
Shashank Hegde, Satyajeet Das, Gautam Salhotra +1
With the increasing availability of open-source robotic data, imitation learning has become a promising approach for both manipulation and locomotion. Diffusion models are now wide…
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Alexander Popov, Alperen Degirmenci, David Wehr +9
We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an…
HyperPPO: A scalable method for finding small policies for robotic control
Shashank Hegde, Zhehui Huang, Gaurav S. Sukhatme
Models with fewer parameters are necessary for the neural control of memory-limited, performant robots. Finding these smaller neural network architectures can be time-consuming. We…
Generating Behaviorally Diverse Policies with Latent Diffusion Models
Shashank Hegde, Sumeet Batra, K. R. Zentner +1
Recent progress in Quality Diversity Reinforcement Learning (QD-RL) has enabled learning a collection of behaviorally diverse, high performing policies. However, these methods typi…
Efficiently Learning Small Policies for Locomotion and Manipulation
Shashank Hegde, Gaurav S. Sukhatme
Neural control of memory-constrained, agile robots requires small, yet highly performant models. We leverage graph hyper networks to learn graph hyper policies trained with off-pol…
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems
Shashank Hegde, Anssi Kanervisto, Aleksei Petrenko
Humans and other intelligent animals evolved highly sophisticated perception systems that combine multiple sensory modalities. On the other hand, state-of-the-art artificial agents…