7 citations · 8 across the 10 of their papers we have counts for
11 papers
In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
Minxing Li, Minghao Han, Weizhi Zhao +10
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem…
Towards robust multimodal 3D object detection via visual foundation models
Ziying Song, Lin Liu, Hongyu Pan +4
Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and camera sensors. However, exi…
ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU
Fan Jiang, Zhaoxu Sun, Mengchao Wang +38
We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA g…
ABot-N1: Toward a General Visual Language Navigation Foundation Model
Ruiyan Gong, Yingnan Guo, Junjun Hu +43
Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typica…
WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models
Ting-Bing Xu, Jiacheng Sui, Zhe Gao +12
Despite rapid progress in interactive world models (IWMs), short-horizon performance does not establish sustained action following, visual stability, physical plausibility, or memo…
ABot-Earth 0.5: Generative 3D Earth Model
Ming Qian, Tianjian Ouyang, Mingchao Sun +25
We present ABot-Earth 0.5, a generative 3D framework designed to synthesize vast, seamless 3D environments from ubiquitous, geospatially referenced satellite imagery. To achieve th…
DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
Ziying Song, Lin Liu, Hongyu Pan +7
Most end-to-end autonomous driving methods rely on imitation learning from single expert demonstrations, often leading to conservative and homogeneous behaviors that limit generali…
Fully Unified Motion Planning for End-to-End Autonomous Driving
Lin Liu, Caiyan Jia, Ziying Song +6
Current end-to-end autonomous driving methods typically learn only from expert planning data collected from a single ego vehicle, severely limiting the diversity of learnable drivi…
Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +7
End-to-end autonomous driving frameworks enable seamless integration of perception and planning but often rely on one-shot trajectory prediction, which may lead to unstable control…
SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection
Lin Liu, Ziying Song, Qiming Xia +4
LiDAR-based sparse 3D object detection plays a crucial role in autonomous driving applications due to its computational efficiency advantages. Existing methods either use the featu…
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection
Ziying Song, Hongyu Pan, Feiyang Jia +8
In the field of 3D object detection tasks, fusing heterogeneous features from LiDAR and camera sensors into a unified Bird's Eye View (BEV) representation is a widely adopted parad…