16 citations · 48 across the 11 of their papers we have counts for
11 papers
FiLM-Nav: Efficient and Generalizable Navigation via VLM Fine-tuning
Naoki Yokoyama, Sehoon Ha
Enabling robotic assistants to navigate complex environments and locate objects described in free-form language is a critical capability for real-world deployment. While foundation…
HM3D-OVON: A Dataset and Benchmark for Open-Vocabulary Object Goal Navigation
Naoki Yokoyama, Ram Ramrakhya, Abhishek Das +2
We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON), a large-scale benchmark that broadens the scope and semantic range of prior Object…
VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
Naoki Yokoyama, Sehoon Ha, Dhruv Batra +2
Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like sea…
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li +28
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation ha…
ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation
Naoki Yokoyama, Alex Clegg, Joanne Truong +6
We present Adaptive Skill Coordination (ASC) -- an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating t…
OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav
Karmesh Yadav, Arjun Majumdar, Ram Ramrakhya +5
We present a single neural network architecture composed of task-agnostic components (ViTs, convolutions, and LSTMs) that achieves state-of-art results on both the ImageNav ("go to…
ViNL: Visual Navigation and Locomotion Over Obstacles
Simar Kareer, Naoki Yokoyama, Dhruv Batra +2
We present Visual Navigation and Locomotion over obstacles (ViNL), which enables a quadrupedal robot to navigate unseen apartments while stepping over small obstacles that lie in i…
Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation
Joanne Truong, Max Rudolph, Naoki Yokoyama +3
If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simu…
Is Mapping Necessary for Realistic PointGoal Navigation?
Ruslan Partsey, Erik Wijmans, Naoki Yokoyama +3
Can an autonomous agent navigate in a new environment without building an explicit map? For the task of PointGoal navigation ('Go to , ') under idealized settings (no RGB-D…
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…
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…