Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
publications
An auxiliary tool for preliminary tests of skin cancer: A self-modifying meta-learning method for clean and noisy data
Published in Sep 24, 2021
Recommended citation: Cao, Yifeng, Yuefan Wu, Zhenyu Tian, and Xuan Yu. "An auxiliary tool for preliminary tests of skin cancer: A self-modifying meta-learning method for clean and noisy data." In 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pp. 172-176. IEEE, 2021.
Download Paper
AnySkin: Plug-and-play Skin Sensing for Robotic Touch
Published in Sep 27, 2024
While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing – versatility, replaceability, and data reusability.
Recommended citation: Bhirangi, Raunaq, Venkatesh Pattabiraman, Enes Erciyes, Yifeng Cao, Tess Hellebrekers, and Lerrel Pinto. "AnySkin: Plug-and-play Skin Sensing for Robotic Touch." arXiv preprint arXiv:2409.08276 (2024).
Download Paper
Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins
Published in Oct 22, 2024
ViSk builds on BAKU, a SOTA policy architecture, and AnySkin – a magnetic tactile sensor. We present comprehensive evaluations on four tasks requiring mm-scale precision: Plug insertion, USB insertion, Card swiping, and Book retrieval, and see an average improvement of ~27.5% when using ViSk over vision-only policies across the four tasks. Additionally, the most exciting part of ViSk is the extent of generalizability of learned policies; it can also perform really well in both unseen spatial configurations of the environment as well as unseen variants of the grasped objects.
Recommended citation: Pattabiraman, Venkatesh, Yifeng Cao, Siddhant Haldar, Lerrel Pinto, and Raunaq Bhirangi. "Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins." arXiv preprint arXiv:2410.17246 (2024).
Download Paper
Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models
Published in Feb 26, 2025
We leverage the generative process of diffusion policies to compute an uncertainty-based metric based on which the autonomous agent can decide to request operator assistance at deployment time, without requiring any operator interaction during training. Additionally, we show that the same method can be used for efficient data collection for fine-tuning diffusion policies in order to improve their autonomous performance.
Recommended citation: He, Zhanpeng, Yifeng Cao, and Matei Ciocarlie. "Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models." arXiv preprint arXiv:2503.01876 (2025).
Download Paper
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.