About the group

  • Human-Robot System (HRS) - Reading Group aims to foster young minds by reading papers by amazing Researchers. So if you want to learn about the new great research or love to have discussions on interesting research ideas, HRS is for you!!!
Fall 2019 Meeting place and time

  • We meet Friday from 11 am - 12 pm in N09.
  • Feel free to join us on zoom if you cannot join in person: Zoom link
Want to join?

  • Just drop us an email at kchanda2@binghamton.edu and we will add you to the group.

Upcoming Paper Reading Sessions

Date Time & Place Leader Paper
04/21/2023 11 am - 12 pm Dave DeFazio TRuihan Yang, Ge Yang, Xiaolong Wang , CVPR 2023
Neural Volumetric Memory for Visual Locomotion Control
04/14/2023 11 am - 12 pm Kishan Chandan Thomas R Groechel*, Amy O’Connell*, Massimiliano Nigro*, and Maja J Mataric , IEEE ROMAN
Reimagining RViz: Multidimensional Augmented Reality Robot Signal Design

Previous Paper Reading Sessions

Date Time & Place Leader Paper
3/31/2023 11 am - 12 pm Eisuke Hirota Xuxin Cheng, Ashish Kumar, Deepak Pathak
Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion
3/24/2023 11 am - 12 pm Yan Ding Jianwei Yang, Zhile Ren, Mingze Xu
Embodied Amodal Recognition: Learning to Move to Perceive Objects
03/17/2023 11 am - 12 pm Xiaohan Zhang Chen Wang, Linxi "Jim" Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar
MimicPlay: Long-Horizon Imitation Learning by Watching Human Play
3/10/2023 11 am - 12 pm Yohei Hayamizu Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas
Guiding Pretraining in Reinforcement Learning with Large Language Models
3/03/2023 11 am - 12 pm Dave DeFazio Hochul Hwang, Tim Xia, Ibrahima Keita, Ken Suzuki, Joydeep Biswas, Sunghoon I. Lee, and Donghyun Kim
System Configuration and Navigation of a Guide Dog Robot: Toward Animal Guide Dog-Level Guiding Work
2/10/2023 11 am - 12 pm Kishan Chandan Caroline Wang, Ishan Durugkar, Elad Liebman, Peter Stone
DM2: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching