71 citations · 160 across the 17 of their papers we have counts for
6 papers · 1 filter
Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation
Yifan Zhou, Shubham Sonawani, Mariano Phielipp +2
Language-conditioned policies allow robots to interpret and execute human instructions. Learning such policies requires a substantial investment with regards to time and compute re…
Group SELFIES: A Robust Fragment-Based Molecular String Representation
Austin Cheng, Andy Cai, Santiago Miret +3
We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustne…
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Brandon Trabucco, Mariano Phielipp, Glen Berseth
The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate t…
Offline Policy Comparison with Confidence: Benchmarks and Baselines
Anurag Koul, Mariano Phielipp, Alan Fern
Decision makers often wish to use offline historical data to compare sequential-action policies at various world states. Importantly, computational tools should produce confidence…
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp +2
Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supe…
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Hassam Sheikh, Kizza Frisbee, Mariano Phielipp
Application of ensemble of neural networks is becoming an imminent tool for advancing the state-of-the-art in deep reinforcement learning algorithms. However, training these large…