10 citations · 21 across the 7 of their papers we have counts for
11 papers
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…
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…
Instance based Generalization in Reinforcement Learning
Martin Bertran, Natalia Martinez, Mariano Phielipp +1
Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels.…
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp +3
Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., moti…
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos
Ajay Kumar Tanwani, Pierre Sermanet, Andy Yan +3
Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we lear…