173 citations · 221 across the 8 of their papers we have counts for
8 papers
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Andy Zeng, Maria Attarian, Brian Ichter +10
Large pretrained (e.g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barel…
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
Ryan Hoque, Kaushik Shivakumar, Shrey Aeron +7
Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware. Using Reach, a cloud r…
Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators
Alex Tong Lin, Adrian S. Wong, Robert Martin +2
We provide a method to identify system parameters of dynamical systems, called ID-ODE -- Inference by Differentiation and Observing Delay Embeddings. In this setting, we are given…
Implicit Behavioral Cloning
Pete Florence, Corey Lynch, Andy Zeng +7
We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly…
Robust Forecasting using Predictive Generalized Synchronization in Reservoir Computing
Jason A. Platt, Adrian S. Wong, Randall Clark +2
Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting timeseries data. As with all RNNs, selecting the hyperparameters presents a challenge whe…
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization
Jason A. Platt, Adrian Wong, Randall Clark +2
Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting time series data. As with all RNNs, selecting the hyperparameters presents a challenge wh…
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
Zheng Fang, Adrian S. Wong, Kangbo Hao +2
In statistical data assimilation (SDA) and supervised machine learning (ML), we wish to transfer information from observations to a model of the processes underlying those observat…
Precision Annealing Monte Carlo Methods for Statistical Data Assimilation: Metropolis-Hastings Procedures
Adrian S. Wong, Kangbo Hao, Zheng Fang +1
Statistical Data Assimilation (SDA) is the transfer of information from field or laboratory observations to a user selected model of the dynamical system producing those observatio…