233 citations · 451 across the 42 of their papers we have counts for
6 papers · 1 filter
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
Sudharshan Suresh, Haozhi Qi, Tingfan Wu +9
To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, suc…
TaskMet: Task-Driven Metric Learning for Model Learning
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam +1
Deep learning models are often deployed in downstream tasks that the training procedure may not be aware of. For example, models solely trained to achieve accurate predictions may…
Perceiving Extrinsic Contacts from Touch Improves Learning Insertion Policies
Carolina Higuera, Joseph Ortiz, Haozhi Qi +3
Robotic manipulation tasks such as object insertion typically involve interactions between object and environment, namely extrinsic contacts. Prior work on Neural Contact Fields (N…
Decentralization and Acceleration Enables Large-Scale Bundle Adjustment
Taosha Fan, Joseph Ortiz, Ming Hsiao +4
Scaling to arbitrarily large bundle adjustment problems requires data and compute to be distributed across multiple devices. Centralized methods in prior works are only able to sol…
USA-Net: Unified Semantic and Affordance Representations for Robot Memory
Benjamin Bolte, Austin Wang, Jimmy Yang +3
In order for robots to follow open-ended instructions like "go open the brown cabinet over the sink", they require an understanding of both the scene geometry and the semantics of…
Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models
Carolina Higuera, Byron Boots, Mustafa Mukadam
Simulating vision-based tactile sensors enables learning models for contact-rich tasks when collecting real world data at scale can be prohibitive. However, modeling the optical re…