Search Results for author: Suraj Nair

Found 30 papers, 10 papers with code

Synthetic Cross-language Information Retrieval Training Data

no code implementations29 Apr 2023 James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham, Orion Weller, Marc Mason, Suraj Nair, Scott Miller

By repeating this process, collections of arbitrary size can be created in the style of MS MARCO but using naturally-occurring documents in any desired genre and domain of discourse.

Information Retrieval Language Modelling +4

Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets

no code implementations18 Apr 2023 Maximilian Du, Suraj Nair, Dorsa Sadigh, Chelsea Finn

Concretely, we propose a simple approach that uses a small amount of downstream expert data to selectively query relevant behaviors from an offline, unlabeled dataset (including many sub-optimal behaviors).

Few-Shot Imitation Learning Imitation Learning +2

Language-Driven Representation Learning for Robotics

2 code implementations24 Feb 2023 Siddharth Karamcheti, Suraj Nair, Annie S. Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, Percy Liang

First, we demonstrate that existing representations yield inconsistent results across these tasks: masked autoencoding approaches pick up on low-level spatial features at the cost of high-level semantics, while contrastive learning approaches capture the opposite.

Contrastive Learning Imitation Learning +2

Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters

no code implementations20 Dec 2022 Eugene Yang, Suraj Nair, Dawn Lawrie, James Mayfield, Douglas W. Oard

By adding adapters pretrained on language tasks for a specific language with task-specific adapters, prior work has shown that the adapter-enhanced models perform better than fine-tuning the entire model when transferring across languages in various NLP tasks.

Information Retrieval Language Modelling +1

Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning

no code implementations30 May 2022 Maximilian Du, Olivia Y. Lee, Suraj Nair, Chelsea Finn

In a set of simulated tasks, we find that our system benefits from using audio, and that by using online interventions we are able to improve the success rate of offline imitation learning by ~20%.

Imitation Learning

R3M: A Universal Visual Representation for Robot Manipulation

1 code implementation23 Mar 2022 Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta

We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks.

Contrastive Learning Robot Manipulation

Cross-language Information Retrieval

no code implementations10 Nov 2021 Petra Galuščáková, Douglas W. Oard, Suraj Nair

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find.

Information Retrieval Retrieval

FitVid: High-Capacity Pixel-Level Video Prediction

no code implementations29 Sep 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan

Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks.

Image Augmentation Video Prediction +1

Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

no code implementations21 Sep 2021 Bohan Wu, Suraj Nair, Li Fei-Fei, Chelsea Finn

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks.

Model-based Reinforcement Learning reinforcement-learning +1

Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation

no code implementations2 Sep 2021 Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter, Silvio Savarese, Chelsea Finn

However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks.

On the Opportunities and Risks of Foundation Models

3 code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

FitVid: Overfitting in Pixel-Level Video Prediction

1 code implementation24 Jun 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan

There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions.

Image Augmentation Video Generation +1

Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos

no code implementations31 Mar 2021 Annie S. Chen, Suraj Nair, Chelsea Finn

We find that by leveraging diverse human datasets, this reward function (a) can generalize zero shot to unseen environments, (b) generalize zero shot to unseen tasks, and (c) can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.

Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

no code implementations CVPR 2021 Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn

Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction.

Video Prediction

Model-Based Visual Planning with Self-Supervised Functional Distances

1 code implementation ICLR 2021 Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot.

reinforcement-learning Reinforcement Learning (RL)

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

2 code implementations29 Oct 2020 Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, Ken Goldberg

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.

reinforcement-learning Reinforcement Learning (RL) +1

Batch Exploration with Examples for Scalable Robotic Reinforcement Learning

1 code implementation22 Oct 2020 Annie S. Chen, HyunJi Nam, Suraj Nair, Chelsea Finn

Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human provided images of important states.

Offline RL reinforcement-learning +1

Goal-Aware Prediction: Learning to Model What Matters

no code implementations ICML 2020 Suraj Nair, Silvio Savarese, Chelsea Finn

In this paper, we propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space, resulting in a learning objective that more closely matches the downstream task.

MATERIALizing Cross-Language Information Retrieval: A Snapshot

no code implementations LREC 2020 Petra Galuscakova, Douglas Oard, Joe Barrow, Suraj Nair, Shing Han-Chin, Elena Zotkina, Esk, Ramy er, Rui Zhang

At about the midpoint of the IARPA MATERIAL program in October 2019, an evaluation was conducted on systems{'} abilities to find Lithuanian documents based on English queries.

Information Retrieval Retrieval

RoboNet: Large-Scale Multi-Robot Learning

no code implementations24 Oct 2019 Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn

This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically large amounts of data for each separate experiment?

Test Video Prediction

Causal Induction from Visual Observations for Goal Directed Tasks

2 code implementations3 Oct 2019 Suraj Nair, Yuke Zhu, Silvio Savarese, Li Fei-Fei

Causal reasoning has been an indispensable capability for humans and other intelligent animals to interact with the physical world.

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation

1 code implementation ICLR 2020 Suraj Nair, Chelsea Finn

Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only self-supervision, reaching novel goals in cluttered scenes with unseen objects.

Self-Supervised Learning Video Prediction

Time Reversal as Self-Supervision

no code implementations2 Oct 2018 Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar

We test our method on the domain of assembly, specifically the mating of tetris-style block pairs.


Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration

no code implementations CVPR 2019 De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles

We hypothesize that to successfully generalize to unseen complex tasks from a single video demonstration, it is necessary to explicitly incorporate the compositional structure of the tasks into the model.

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

1 code implementation4 Oct 2017 Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, Silvio Savarese

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction.

Few-Shot Learning Program induction +1

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