38 citations · 87 across the 13 of their papers we have counts for
16 papers
PerturBot: Breaking Shortcut Priors in Vision-Language-Action Models with Perturbative Training
Mingyu Liu, Chonghao Sima, Tianjian Feng +4
A vision--language--action (VLA) policy can complete complex tasks while ignoring the evidence that should determine its actions. An object held near the wrist camera can displace…
: Resource-Aware Robust Manipulation via Taming Distributional Inconsistencies
Checheng Yu, Chonghao Sima, Gangcheng Jiang +14
High-reliability long-horizon robotic manipulation has traditionally relied on large-scale data and compute to understand complex real-world dynamics. However, we identify that the…
ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models
Shadi Hamdan, Chonghao Sima, Zetong Yang +2
How can we benefit from large models without sacrificing inference speed, a common dilemma in self-driving systems? A prevalent solution is a dual-system architecture, employing a…
Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
Chonghao Sima, Kashyap Chitta, Zhiding Yu +5
How can we rely on an end-to-end autonomous vehicle's complex decision-making system during deployment? One common solution is to have a ``fallback layer'' that checks the planned…
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
AgiBot World Contributors, AgiBot-World-Contributors, Qingwen Bu +50
We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 millio…
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives
Shaoyuan Xie, Lingdong Kong, Yuhao Dong +5
Recent advancements in Vision-Language Models (VLMs) have sparked interest in their use for autonomous driving, particularly in generating interpretable driving decisions through n…
Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving
Kairui Ding, Boyuan Chen, Yuchen Su +8
End-to-end architectures in autonomous driving (AD) face a significant challenge in interpretability, impeding human-AI trust. Human-friendly natural language has been explored for…
DriveLM: Driving with Graph Visual Question Answering
Chonghao Sima, Katrin Renz, Kashyap Chitta +7
We study how vision-language models (VLMs) trained on web-scale data can be integrated into end-to-end driving systems to boost generalization and enable interactivity with human u…
Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
Linyan Huang, Zhiqi Li, Chonghao Sima +4
Current research is primarily dedicated to advancing the accuracy of camera-only 3D object detectors (apprentice) through the knowledge transferred from LiDAR- or multi-modal-based…
Scene as Occupancy
Chonghao Sima, Wenwen Tong, Tai Wang +8
Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-a…
Sparse Dense Fusion for 3D Object Detection
Yulu Gao, Chonghao Sima, Shaoshuai Shi +3
With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection. Although multiple fusion approaches have been proposed, they can be cl…
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Huijie Wang, Tianyu Li, Yang Li +13
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scen…
Planning-oriented Autonomous Driving
Yihan Hu, Jiazhi Yang, Li Chen +13
Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks an…
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
Hongyang Li, Chonghao Sima, Jifeng Dai +19
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches…
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
Zhiqi Li, Wenhai Wang, Hongyang Li +5
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new f…
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Li Chen, Chonghao Sima, Yang Li +8
Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.). Previo…