1 citations · 2 across the 7 of their papers we have counts for
7 papers
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning
Haoran Geng, Feishi Wang, Songlin Wei +34
Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges i…
ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface
Fangchen Liu, Chuanyu Li, Yihua Qin +3
Tactile information plays a crucial role for humans and robots to interact effectively with their environment, particularly for tasks requiring the understanding of contact propert…
Geometric Retargeting: A Principled, Ultrafast Neural Hand Retargeting Algorithm
Zhao-Heng Yin, Changhao Wang, Luis Pineda +4
We introduce Geometric Retargeting (GeoRT), an ultrafast, and principled neural hand retargeting algorithm for teleoperation, developed as part of our recent Dexterity Gen (DexGen)…
OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction
Huang Huang, Fangchen Liu, Letian Fu +5
Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visio…
Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs
Nikhil Mishra, Maximilian Sieb, Pieter Abbeel +1
Deep learning methods for perception are the cornerstone of many robotic systems. Despite their potential for impressive performance, obtaining real-world training data is expensiv…
Toward a Science of Autonomy for Physical Systems: Paths
Pieter Abbeel, Ken Goldberg, Gregory Hager +1
An Autonomous Physical System (APS) will be expected to reliably and independently evaluate, execute, and achieve goals while respecting surrounding rules, laws, or conventions. In…