no code implementations • 18 May 2025 • Zhengyi Luo, Chen Tessler, Toru Lin, Ye Yuan, Tairan He, Wenli Xiao, Yunrong Guo, Gal Chechik, Kris Kitani, Linxi Fan, Yuke Zhu
Human behavior is fundamentally shaped by visual perception -- our ability to interact with the world depends on actively gathering relevant information and adapting our movements accordingly.
no code implementations • 27 Feb 2025 • Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu
This work investigates the key challenges in applying reinforcement learning to solve a collection of contact-rich manipulation tasks on a humanoid embodiment.
1 code implementation • 25 Apr 2024 • Toru Lin, Yu Zhang, Qiyang Li, Haozhi Qi, Brent Yi, Sergey Levine, Jitendra Malik
Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing.
no code implementations • 4 Mar 2024 • Toru Lin, Zhao-Heng Yin, Haozhi Qi, Pieter Abbeel, Jitendra Malik
Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, due to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system.
1 code implementation • NeurIPS 2023 • Toru Lin, Allan Jabri
We show how this perspective naturally leads to a unified view on existing intrinsic reward approaches: they are special cases of conditional prediction, where the estimation of novelty can be seen as pseudo-likelihood estimation with different mask distributions.
no code implementations • NeurIPS 2021 • Toru Lin, Minyoung Huh, Chris Stauffer, Ser-Nam Lim, Phillip Isola
Communication requires having a common language, a lingua franca, between agents.
1 code implementation • ICML 2020 • Yunzhu Li, Toru Lin, Kexin Yi, Daniel M. Bear, Daniel L. K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba
The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models.
no code implementations • 15 Sep 2019 • Yilun Du, Toru Lin, Igor Mordatch
We provide an online algorithm to train EBMs while interacting with the environment, and show that EBMs allow for significantly better online learning than corresponding feed-forward networks.