39 citations · 87 across the 19 of their papers we have counts for
13 papers · 1 filter
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models
Chenyuan Zhou, Yining Hong, Huang Huang +9
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitl…
Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model
Celina Shiyu Wang, Yiqi Zhao, Junjie Ye +2
Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requiremen…
SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
Songlin Wei, Zhenhao Ni, Jie Liu +9
Humanoid foundation models are advancing faster than we can evaluate them. While real-world testing is expensive and difficult to reproduce, existing simulation benchmarks focus pr…
PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies
Arhan Jain, Mingtong Zhang, Kanav Arora +11
A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically cha…
Robot Learning from a Physical World Model
Jiageng Mao, Sicheng He, Hao-Ning Wu +9
We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic…
Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow +11
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a…
Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey
Chaoran Xiong, Yulong Huang, Fangwen Yu +4
Embodied navigation (EN) advances traditional navigation by enabling robots to perform complex egocentric tasks through sensing, social, and motion intelligence. In contrast to cla…
ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation
Enyu Zhao, Vedant Raval, Hejia Zhang +5
Vision-Language Models (VLMs) have revolutionized artificial intelligence and robotics due to their commonsense reasoning capabilities. In robotic manipulation, VLMs are used prima…
Domain-Conditioned Scene Graphs for State-Grounded Task Planning
Jonas Herzog, Jiangpin Liu, Yue Wang
Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4o. To address grounding issues of such models, it has been suggested to split th…
Learning from Massive Human Videos for Universal Humanoid Pose Control
Jiageng Mao, Siheng Zhao, Siqi Song +7
Scalable learning of humanoid robots is crucial for their deployment in real-world applications. While traditional approaches primarily rely on reinforcement learning or teleoperat…
LiLoc: Lifelong Localization using Adaptive Submap Joining and Egocentric Factor Graph
Yixin Fang, Yanyan Li, Kun Qian +3
This paper proposes a versatile graph-based lifelong localization framework, LiLoc, which enhances its timeliness by maintaining a single central session while improves the accurac…
RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
Yuxuan Kuang, Junjie Ye, Haoran Geng +5
This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation, dubbed RAM, featuring generalizability across various objects, environments, and embodiment…
Driving Everywhere with Large Language Model Policy Adaptation
Boyi Li, Yue Wang, Jiageng Mao +4
Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs)…