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Tongtong Cao

13 accepted papers

2026

CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance

Rui Heng Yang, Xuan Zhao, Léo Maxime Brunswic, Montgomery Tucker Alban, Matéo Clémente, Tongtong Cao +2
ICRA 2026poster

In robotics, diffusion models can capture multi-modal trajectories from demonstrations, making them a transformative approach in imitation learning. However, achieving optimal performance following this regiment requires a large-scale dataset, which is costly to obtain, especially for challenging ta…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

HIPPo: Harnessing Image-To-3D Priors for Model-Free Zero-Shot 6D Pose Estimation

Yibo Liu, Zhaodong Jiang, Binbin Xu, Guile Wu, Yuan Ren, Tongtong Cao +4
ICRA 2026poster

This work focuses on the problem of 6D pose estimation for novel objects when a reference 3D model or posed reference images are not available. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

OpenPyRo-A1: An Open Python-Based Low-Cost Bimanual Robot for Embodied AI

Helong Huang, Christopher E. Mower, Guowei Huang, Sarthak Das, Magnus Dierking, Guangyuan Luo +20
RA-L 2026

Many real-world tasks, such as assembly, cooking, and object handovers, require bi-manual coordination. Learning such skills via imitation remains challenging due to dataset scarcity, mainly caused by the high cost of bi-manual robotic platforms and barriers to entry in robotics software. To address

Cited by 1Source ↗Scholar ↗
2026

OpenPyRo-A1: An Open Python-Based Low-Cost Bimanual Robot for Embodied AI

Helong Huang, Christopher Edwin Mower, Guowei Huang, Sarthak Das, Magnus Dierking, Guangyuan Luo +20
ICRA 2026poster

Many real-world tasks, such as assembly, cooking, and object handovers, require bi-manual coordination. Learning such skills via imitation remains challenging due to dataset scarcity, mainly caused by the high cost of bi-manual robotic platforms and barriers to entry in robotics software. To address…

Cited by 0Source ↗Scholar ↗
2026

Uncertainty-Aware Gaussian Map for Vision-Language Navigation

Jianzhe Gao, Rui Liu, Yuxuan Xu, Tongtong Cao, Yingxue Zhang, Zhanguang Zhang +3
ICLR 2026poster

Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agents commonly encounter perceptual uncertainty, such as insufficient evidence for reliable grounding or ambiguity in interpreting spatial cues, yet th…

Cited by 0Source ↗Scholar ↗
2025

AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction

Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma, Tongtong Cao, Dongfeng Bai, Yuan Ren +1
ICRA 2025

Realistic scene reconstruction and view synthesis are essential for advancing autonomous driving systems by simulating safety-critical scenarios. 3D Gaussian Splatting (3DGS) excels in real-time rendering and static scene reconstructions but struggles with modeling driving scenarios due to complex b

Cited by 49Source ↗code ↗Scholar ↗
2025

ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models

Lingfeng Zhang, Yuening Wang, Hongjian Gu, Atia Hamidizadeh, Zhanguang Zhang, Yuecheng Liu +9
IROS 2025

Recent advancements in Large Language Models (LLMs) have catalyzed numerous efforts to apply these technologies to embodied tasks, with a particular focus on high-level task planning and task decomposition. LLMs face challenges in understanding the physical world, especially regarding spatial, tempo

Cited by 11Source ↗code ↗Scholar ↗
2025

HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation

Yibo Liu, Zhaodong Jiang, Binbin Xu, Guile Wu, Yuan Ren, Tongtong Cao +4
RA-L 2025

This work focuses on the problem of 6D pose estimation for novel objects when a reference 3D model or posed reference images are not available. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is

Cited by 4Source ↗Scholar ↗
2025

UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation

Zhaodong Jiang, Ashish Sinha, Tongtong Cao, Yuan Ren, Bingbing Liu, Binbin Xu
CoRL 2025poster

Estimating the 6D pose of novel objects is a fundamental yet challenging problem in robotics, often relying on access to object CAD models. However, acquiring such models can be costly and impractical. Recent approaches aim to bypass this requirement by leveraging strong priors from founda…

Cited by 0Source ↗Scholar ↗
2025

Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning

Fazel Arasteh, Mohammed Elmahgiubi, Behzad Khamidehi, Hamidreza Mirkhani, Weize Zhang, Tongtong Cao +1
ICRA 2025

The planning problem constitutes a fundamental aspect of the autonomous driving framework. Recent strides in representation learning have empowered vehicles to comprehend their surrounding environments, thereby facilitating the integration of learning-based planning strategies. Among these approache

Cited by 7Source ↗Scholar ↗
2023

GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds

Ziyu Li, Jingming Guo, Tongtong Cao, Liu Bingbing, Wankou Yang
ICCV 2023poster

LiDAR-based 3D detection has made great progress in recent years. However, the performance of 3D detectors is considerably limited when deployed in unseen environments, owing to the severe domain gap problem. Existing domain adaptive 3D detection methods do not adequately consider the problem of the…

Cited by 15PDF ↗code ↗Scholar ↗
2023

Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer

Guile Wu, Tongtong Cao, Bingbing Liu, Xingxin Chen, Yuan Ren
ICCV 2023poster

Contemporary LiDAR-based 3D object detection methods mostly focus on single-domain learning or cross-domain adaptive learning. However, for autonomous driving systems, optimizing a specific LiDAR-based 3D object detector for each domain is costly and lacks of scalability in real-world deployment. It…

Cited by 8PDF ↗code ↗Scholar ↗
2022

How to Build a Curb Dataset with LiDAR Data for Autonomous Driving

Dongfeng Bai, Tongtong Cao, Jingming Guo, Bingbing Liu
ICRA 2022poster

Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, came…

Cited by 7Source ↗Scholar ↗