Search Results for author: Dhiraj Gandhi

Found 14 papers, 7 papers with code

Beyond Games: Bringing Exploration to Robots in Real-world

no code implementations ICLR 2019 Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta

But most importantly, we are able to implement an exploration policy on a robot which learns to interact with objects completely from scratch just using data collected via the differentiable exploration module.

Efficient Exploration

droidlet: modular, heterogenous, multi-modal agents

1 code implementation25 Jan 2021 Anurag Pratik, Soumith Chintala, Kavya Srinet, Dhiraj Gandhi, Rebecca Qian, Yuxuan Sun, Ryan Drew, Sara Elkafrawy, Anoushka Tiwari, Tucker Hart, Mary Williamson, Abhinav Gupta, Arthur Szlam

In recent years, there have been significant advances in building end-to-end Machine Learning (ML) systems that learn at scale.

Visual Imitation Made Easy

no code implementations11 Aug 2020 Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, Lerrel Pinto

We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector.

Imitation Learning Structure from Motion

Swoosh! Rattle! Thump! -- Actions that Sound

no code implementations3 Jul 2020 Dhiraj Gandhi, Abhinav Gupta, Lerrel Pinto

In this work, we perform the first large-scale study of the interactions between sound and robotic action.

Object Goal Navigation using Goal-Oriented Semantic Exploration

1 code implementation NeurIPS 2020 Devendra Singh Chaplot, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov

We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category.

Robot Navigation

Learning to Explore using Active Neural SLAM

1 code implementation ICLR 2020 Devendra Singh Chaplot, Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta, Ruslan Salakhutdinov

The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies).

PointGoal Navigation

Object-centric Forward Modeling for Model Predictive Control

1 code implementation8 Oct 2019 Yufei Ye, Dhiraj Gandhi, Abhinav Gupta, Shubham Tulsiani

We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals.

Self-Supervised Exploration via Disagreement

1 code implementation10 Jun 2019 Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta

In this paper, we propose a formulation for exploration inspired by the work in active learning literature.

Active Learning Efficient Exploration +1

Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

no code implementations NeurIPS 2018 Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi, Lerrel Pinto

The models trained with our home dataset showed a marked improvement of 43. 7% over a baseline model trained with data collected in lab.

Robotic Grasping

Learning to Grasp Without Seeing

no code implementations10 May 2018 Adithyavairavan Murali, Yin Li, Dhiraj Gandhi, Abhinav Gupta

We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge.

Object Localization

CASSL: Curriculum Accelerated Self-Supervised Learning

no code implementations4 Aug 2017 Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, Abhinav Gupta

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces.

Curriculum Learning Self-Supervised Learning

The Curious Robot: Learning Visual Representations via Physical Interactions

no code implementations5 Apr 2016 Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, Abhinav Gupta

We argue that biological agents use physical interactions with the world to learn visual representations unlike current vision systems which just use passive observations (images and videos downloaded from web).

Image Classification Representation Learning

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