107 citations · 220 across the 9 of their papers we have counts for
10 papers
YOR: Your Own Mobile Manipulator for Generalizable Robotics
Manan H Anjaria, Mehmet Enes Erciyes, Vedant Ghatnekar +11
Recent advances in robot learning have generated significant interest in capable platforms that may eventually approach human-level competence. This interest, combined with the com…
Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models
Zichen Jeff Cui, Omar Rayyan, Haritheja Etukuru +16
The prevalent paradigm in robot learning attempts to generalize across environments, embodiments, and tasks with language prompts at runtime. A fundamental tension limits this appr…
Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments
Haritheja Etukuru, Norihito Naka, Zijin Hu +7
Robot models, particularly those trained with large amounts of data, have recently shown a plethora of real-world manipulation and navigation capabilities. Several independent effo…
On Bringing Robots Home
Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru +4
Throughout history, we have successfully integrated various machines into our homes. Dishwashers, laundry machines, stand mixers, and robot vacuums are a few recent examples. Howev…
From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data
Zichen Jeff Cui, Yibin Wang, Nur Muhammad Mahi Shafiullah +1
While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics…
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory
Nur Muhammad Mahi Shafiullah, Chris Paxton, Lerrel Pinto +2
We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localiz…
Behavior Transformers: Cloning modes with one stone
Nur Muhammad Mahi Shafiullah, Zichen Jeff Cui, Ariuntuya Altanzaya +1
While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large, human-g…
One After Another: Learning Incremental Skills for a Changing World
Nur Muhammad Shafiullah, Lerrel Pinto
Reward-free, unsupervised discovery of skills is an attractive alternative to the bottleneck of hand-designing rewards in environments where task supervision is scarce or expensive…
The Surprising Effectiveness of Representation Learning for Visual Imitation
Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam +1
While visual imitation learning offers one of the most effective ways of learning from visual demonstrations, generalizing from them requires either hundreds of diverse demonstrati…
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
Kai Y. Xiao, Vincent Tjeng, Nur Muhammad Shafiullah +1
We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial pertur…