activity
20162025
most citedTarget-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

163 citations · 371 across the 12 of their papers we have counts for

collaborators
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9 papers · 1 filter

cs.RO2025★ 3 cited

SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

Zhengyi Luo, Ye Yuan, Tingwu Wang +25

Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid contro…

cs.RO2025★ 5 cited

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

NVIDIA, :, Johan Bjorck +40

General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist…

cs.RO2024

BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation

Rutav Shah, Albert Yu, Yifeng Zhu +2

To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different floors, and interactin…

cs.RO2023★ 103 cited

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…

cs.RO2022

Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment

Huihan Liu, Soroush Nasiriany, Lance Zhang +2

With the rapid growth of computing powers and recent advances in deep learning, we have witnessed impressive demonstrations of novel robot capabilities in research settings. Noneth…

cs.RO2022★ 13 cited

VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors

Yifeng Zhu, Abhishek Joshi, Peter Stone +1

We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop visuomotor policies for robot manipulation. Our approach constructs object-centric represe…