8 citations · 14 across the 5 of their papers we have counts for
5 papers
See to Touch: Learning Tactile Dexterity through Visual Incentives
Irmak Guzey, Yinlong Dai, Ben Evans +2
Equipping multi-fingered robots with tactile sensing is crucial for achieving the precise, contact-rich, and dexterous manipulation that humans excel at. However, relying solely on…
Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play
Irmak Guzey, Ben Evans, Soumith Chintala +1
Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics. Most prominent work in this area focuses on learning controllers or policies that either…
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation
Sridhar Pandian Arunachalam, Sneha Silwal, Ben Evans +1
Optimizing behaviors for dexterous manipulation has been a longstanding challenge in robotics, with a variety of methods from model-based control to model-free reinforcement learni…
Context is Everything: Implicit Identification for Dynamics Adaptation
Ben Evans, Abitha Thankaraj, Lerrel Pinto
Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables su…
BAM: Bayes with Adaptive Memory
Josue Nassar, Jennifer Brennan, Ben Evans +1
Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non stationary…