2 citations · 5 across the 8 of their papers we have counts for
8 papers
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
David D. Yuan, Tony Z. Zhao, Kaylee Burns +1
While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learnin…
RoboCrowd: Scaling Robot Data Collection through Crowdsourcing
Suvir Mirchandani, David D. Yuan, Kaylee Burns +4
In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large q…
Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
Kyle Hsu, Jubayer Ibn Hamid, Kaylee Burns +2
Inductive biases are crucial in disentangled representation learning for narrowing down an underspecified solution set. In this work, we consider endowing a neural network autoenco…
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Kaylee Burns, Ajinkya Jain, Keegan Go +4
Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise moveme…
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?
Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid +3
Inspired by the success of transfer learning in computer vision, roboticists have investigated visual pre-training as a means to improve the learning efficiency and generalization…
Neural Functional Transformers
Allan Zhou, Kaien Yang, Yiding Jiang +5
The recent success of neural networks as implicit representation of data has driven growing interest in neural functionals: models that can process other neural networks as input b…
Permutation Equivariant Neural Functionals
Allan Zhou, Kaien Yang, Kaylee Burns +5
This work studies the design of neural networks that can process the weights or gradients of other neural networks, which we refer to as neural functional networks (NFNs). Despite…
Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning
Kaylee Burns, Christopher D. Manning, Li Fei-Fei
Although virtual agents are increasingly situated in environments where natural language is the most effective mode of interaction with humans, these exchanges are rarely used as a…