6.2k citations · 14.2k across the 134 of their papers we have counts for
16 papers · 1 filter
Detecting Heavy Higgs Bosons from Natural SUSY at a 100 TeV Hadron Collider
Howard Baer, Vernon Barger, Rishabh Jain +3
Supersymmetric models with radiatively-driven naturalness (RNS) enjoy low electroweak fine-tuning whilst respecting LHC search limits on gluinos and top squarks and allowing for $m…
Searching for Charged Higgs Bosons via at Linear Colliders
Wei-Shu Hou, Rishabh Jain, Tanmoy Modak
We study a search for the charged Higgs boson via at the 500 GeV ILC. In a general two Higgs doublet model without symmetry, extra Yu…
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation
Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal +2
Natural language instructions for visual navigation often use scene descriptions (e.g., "bedroom") and object references (e.g., "green chairs") to provide a breadcrumb trail to a g…
Waypoint Models for Instruction-guided Navigation in Continuous Environments
Jacob Krantz, Aaron Gokaslan, Dhruv Batra +2
Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with…
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Kristen Grauman, Andrew Westbury, Eugene Byrne +82
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outd…
Realistic PointGoal Navigation via Auxiliary Losses and Information Bottleneck
Guillermo Grande, Dhruv Batra, Erik Wijmans
We propose a novel architecture and training paradigm for training realistic PointGoal Navigation -- navigating to a target coordinate in an unseen environment under actuation and…
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
Santhosh K. Ramakrishnan, Aaron Gokaslan, Erik Wijmans +10
We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene…
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge
Naoki Yokoyama, Qian Luo, Dhruv Batra +1
Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation. Ho…
The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation
Xiaoming Zhao, Harsh Agrawal, Dhruv Batra +1
It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments.…
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
Andrew Szot, Alex Clegg, Eric Undersander +18
We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contr…
Model-Advantage and Value-Aware Models for Model-Based Reinforcement Learning: Bridging the Gap in Theory and Practice
Nirbhay Modhe, Harish Kamath, Dhruv Batra +1
This work shows that value-aware model learning, known for its numerous theoretical benefits, is also practically viable for solving challenging continuous control tasks in prevale…
Collider Prospects for Muon in General Two Higgs Doublet Model
Wei-Shu Hou, Rishabh Jain, Chung Kao +2
Recent progress on muon measurement prompts one to take it even more seriously. In the general two Higgs doublet model that allows extra Yukawa couplings, we take a simplifie…
Auxiliary Tasks and Exploration Enable ObjectNav
Joel Ye, Dhruv Batra, Abhishek Das +1
ObjectGoal Navigation (ObjectNav) is an embodied task wherein agents are to navigate to an object instance in an unseen environment. Prior works have shown that end-to-end ObjectNa…
Large Batch Simulation for Deep Reinforcement Learning
Brennan Shacklett, Erik Wijmans, Aleksei Petrenko +4
We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of ove…
Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation
Naoki Yokoyama, Sehoon Ha, Dhruv Batra
We present Success weighted by Completion Time (SCT), a new metric for evaluating navigation performance for mobile robots. Several related works on navigation have used Success we…
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
Lina Mezghani, Sainbayar Sukhbaatar, Thibaut Lavril +4
In this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization…