Search Results for author:
Found 8 papers, 4 papers with code
BridgeData V2: A Dataset for Robot Learning at Scale
By publicly sharing BridgeData V2 and our pre-trained models, we aim to accelerate research in scalable robot learning methods.
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
The key idea is to apply a variant of contrastive learning to time series data.
Bayesian Meta-Learning Through Variational Gaussian Processes
This problem setting can be extended to the Bayesian context, wherein rather than predicting a single label for each query data point, a model predicts a distribution of labels capturing its uncertainty.
A Hierarchical Approach to Scaling Batch Active Search Over Structured Data
As modern data sets grow, so does the need to scale active search to large data sets and batch sizes.
Learning Multimodal Rewards from Rankings
However, expert feedback is often assumed to be drawn from an underlying unimodal reward function.
Active Reward Learning from Online Preferences
Robot policies need to adapt to human preferences and/or new environments.
Toward Grounded Commonsense Reasoning
We additionally illustrate our approach with a robot on 2 carefully designed surfaces.
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Our method achieves robust performance in the real world by learning an embedding from the labeled data that aligns language not to the goal image, but rather to the desired change between the start and goal images that the instruction corresponds to.
