8 citations · 11 across the 6 of their papers we have counts for
7 papers
RoboVista: Evaluating Vision Language Models for Diverse Robot Applications
Shuangyu Xie, Kaiyuan Chen, Ziyang Chen +8
Diverse applications for robotics, such as industry and agriculture, require robots to operate across various embodiments, changing visual conditions, and complex planning. Vision-…
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…
Blox-Net: Generative Design-for-Robot-Assembly Using VLM Supervision, Physics Simulation, and a Robot with Reset
Andrew Goldberg, Kavish Kondap, Tianshuang Qiu +7
Generative AI systems have shown impressive capabilities in creating text, code, and images. Inspired by the rich history of research in industrial ''Design for Assembly'', we intr…
Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables
Jonathan Wang, Huang Huang, Vincent Lim +5
Dynamic manipulation of free-end cables has applications for cable management in homes, warehouses and manufacturing plants. We present a supervised learning approach for dynamic m…
Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2
Adam Rashid, Chung Min Kim, Justin Kerr +11
Inventory monitoring in homes, factories, and retail stores relies on maintaining data despite objects being swapped, added, removed, or moved. We introduce Lifelong LERF, a method…
A Touch, Vision, and Language Dataset for Multimodal Alignment
Letian Fu, Gaurav Datta, Huang Huang +7
Touch is an important sensing modality for humans, but it has not yet been incorporated into a multimodal generative language model. This is partially due to the difficulty of obta…
Rethinking Patch Dependence for Masked Autoencoders
Letian Fu, Long Lian, Renhao Wang +6
In this work, we examine the impact of inter-patch dependencies in the decoder of masked autoencoders (MAE) on representation learning. We decompose the decoding mechanism for mask…