Search Results for author: Pulkit Agrawal

Found 28 papers, 15 papers with code

An End-to-End Differentiable Framework for Contact-Aware Robot Design

1 code implementation15 Jul 2021 Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal

Existing methods for co-optimization are limited and fail to explore a rich space of designs.

Learning Task Informed Abstractions

1 code implementation29 Jun 2021 Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola

Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features.

Model-based Reinforcement Learning

Residual Model Learning for Microrobot Control

no code implementations1 Apr 2021 Joshua Gruenstein, Tao Chen, Neel Doshi, Pulkit Agrawal

RML provides a general framework for learning from extremely small amounts of interaction data, and our experiments with HAMR clearly demonstrate that RML substantially outperforms existing techniques.

The Low-Rank Simplicity Bias in Deep Networks

1 code implementation18 Mar 2021 Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola

Modern deep neural networks are highly over-parameterized compared to the data on which they are trained, yet they often generalize remarkably well.

Image Classification

Learning to Recover from Failures using Memory

no code implementations1 Jan 2021 Tao Chen, Pulkit Agrawal

Learning from past mistakes is a quintessential aspect of intelligence.

Decision Making Meta-Learning

A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects

no code implementations16 Nov 2020 Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois R. Hogan, Joshua Tenenbaum, Pulkit Agrawal, Alberto Rodriguez

We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i. e. without prior object models.

Graph Attention Motion Planning

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning

no code implementations ICLR 2021 Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum

Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited.

Few-Shot Imitation Learning Imitation Learning +1

AdaScale SGD: A User-Friendly Algorithm for Distributed Training

1 code implementation ICML 2020 Tyler B. Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin

When using large-batch training to speed up stochastic gradient descent, learning rates must adapt to new batch sizes in order to maximize speed-ups and preserve model quality.

Image Classification Machine Translation +3

Exploring Exploration: Comparing Children with RL Agents in Unified Environments

1 code implementation6 May 2020 Eliza Kosoy, Jasmine Collins, David M. Chan, Sandy Huang, Deepak Pathak, Pulkit Agrawal, John Canny, Alison Gopnik, Jessica B. Hamrick

Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.

Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning

1 code implementation23 Dec 2019 Richard Li, Allan Jabri, Trevor Darrell, Pulkit Agrawal

Learning robotic manipulation tasks using reinforcement learning with sparse rewards is currently impractical due to the outrageous data requirements.

Classification in the dark using tactile exploration

no code implementations ICLR 2019 Mayur Mudigonda, Blake Tickell, Pulkit Agrawal

Combining information from different sensory modalities to execute goal directed actions is a key aspect of human intelligence.

Classification General Classification

Superposition of many models into one

2 code implementations NeurIPS 2019 Brian Cheung, Alex Terekhov, Yubei Chen, Pulkit Agrawal, Bruno Olshausen

We present a method for storing multiple models within a single set of parameters.

Learning Instance Segmentation by Interaction

1 code implementation21 Jun 2018 Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik

The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels.

Instance Segmentation Semantic Segmentation

What Will Happen Next? Forecasting Player Moves in Sports Videos

no code implementations ICCV 2017 Panna Felsen, Pulkit Agrawal, Jitendra Malik

A large number of very popular team sports involve the act of one team trying to score a goal against the other.

Curiosity-driven Exploration by Self-supervised Prediction

9 code implementations ICML 2017 Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell

In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether.

Learning to Perform Physics Experiments via Deep Reinforcement Learning

no code implementations6 Nov 2016 Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter Battaglia, Nando de Freitas

When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way.

Learning Visual Predictive Models of Physics for Playing Billiards

no code implementations23 Nov 2015 Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik

The ability to plan and execute goal specific actions in varied, unexpected settings is a central requirement of intelligent agents.

Human Pose Estimation with Iterative Error Feedback

1 code implementation CVPR 2016 Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, Jitendra Malik

Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved impressive performance on a variety of classification tasks using purely feedforward processing.

Pose Estimation Semantic Segmentation

Learning to See by Moving

no code implementations ICCV 2015 Pulkit Agrawal, Joao Carreira, Jitendra Malik

We show that given the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on visual tasks of scene recognition, object recognition, visual odometry and keypoint matching.

Object Recognition Scene Recognition +1

Pixels to Voxels: Modeling Visual Representation in the Human Brain

no code implementations18 Jul 2014 Pulkit Agrawal, Dustin Stansbury, Jitendra Malik, Jack L. Gallant

We find that both classes of models accurately predict brain activity in high-level visual areas, directly from pixels and without the need for any semantic tags or hand annotation of images.

Object Recognition

Analyzing the Performance of Multilayer Neural Networks for Object Recognition

1 code implementation7 Jul 2014 Pulkit Agrawal, Ross Girshick, Jitendra Malik

In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks.

Object Recognition

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