Search Results for author: Ilija Radosavovic

Found 13 papers, 8 papers with code

Learning Humanoid Locomotion with Transformers

no code implementations6 Mar 2023 Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, Koushil Sreenath

We present a sim-to-real learning-based approach for real-world humanoid locomotion.

Learning to Imitate Object Interactions from Internet Videos

no code implementations23 Nov 2022 Austin Patel, Andrew Wang, Ilija Radosavovic, Jitendra Malik

In this paper we make two main contributions: (1) a novel reconstruction technique RHOV (Reconstructing Hands and Objects from Videos), which reconstructs 4D trajectories of both the hand and the object using 2D image cues and temporal smoothness constraints; (2) a system for imitating object interactions in a physics simulator with reinforcement learning.

Real-World Robot Learning with Masked Visual Pre-training

1 code implementation6 Oct 2022 Ilija Radosavovic, Tete Xiao, Stephen James, Pieter Abbeel, Jitendra Malik, Trevor Darrell

Finally, we train a 307M parameter vision transformer on a massive collection of 4. 5M images from the Internet and egocentric videos, and demonstrate clearly the benefits of scaling visual pre-training for robot learning.

Masked Visual Pre-training for Motor Control

1 code implementation11 Mar 2022 Tete Xiao, Ilija Radosavovic, Trevor Darrell, Jitendra Malik

This paper shows that self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels.

Ego4D: Around the World in 3,000 Hours of Egocentric Video

3 code implementations CVPR 2022 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification Ethics

State-Only Imitation Learning for Dexterous Manipulation

no code implementations7 Apr 2020 Ilija Radosavovic, Xiaolong Wang, Lerrel Pinto, Jitendra Malik

To tackle this setting, we train an inverse dynamics model and use it to predict actions for state-only demonstrations.

Imitation Learning

On Network Design Spaces for Visual Recognition

4 code implementations ICCV 2019 Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, Piotr Dollár

Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape.

Neural Architecture Search

Attentive Single-Tasking of Multiple Tasks

2 code implementations CVPR 2019 Kevis-Kokitsi Maninis, Ilija Radosavovic, Iasonas Kokkinos

In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as "single-tasking multiple tasks".

Data Distillation: Towards Omni-Supervised Learning

4 code implementations CVPR 2018 Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, Kaiming He

We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.

Keypoint Detection object-detection +1

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