158 citations · 419 across the 62 of their papers we have counts for
12 papers · 1 filter
Mem4Act: Learning Effective Latent Memory for Long-Horizon Robotic Manipulation
Yun Zhang, Zhiyu Huang, Tianhui Cai +3
Long-horizon robotic manipulation fundamentally requires memory: agents must retain task-relevant information from past observations and interactions and use it to guide future act…
AutoAgent0: An Agentic Runtime for Safe Closed-Loop Driving
Jiageng Liu, Seth Z. Zhao, Zewei Zhou +9
Safe autonomous driving requires more than accurate planning at a single frame: it demands reliable closed-loop execution in dynamic environments. Although end-to-end driving polic…
SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation
Dijie Zhu, Seunghun Oh, Ruopeng Huang +3
Reliable performance evaluation is a central bottleneck for deploying robot-learning policies in real-world conditions. Real-world testing is faithful but costly and difficult to s…
Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis
Chenghao Qian, Nedko Savov, Lingdong Kong +7
Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion. The key limitation of existing methods is the lack of diversit…
nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving
Zhiyu Huang, Johnson Liu, Rui Song +13
Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations, infer agent interactions,…
What-If World: A Causal Benchmark for General World Models in Embodied Scenarios
Kunlin Cai, Rui Song, Jinghuai Zhang +7
Video generation models are increasingly used as world simulators for tasks like driving and robotic manipulation. What matters in these settings is not whether a single video look…
MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
Marco Coscoy, Zewei Zhou, Seth Z. Zhao +9
Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and ne…
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
Rui Song, Tianhui Cai, Markus Gross +5
Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, whi…
EnerGS: Energy-Based Gaussian Splatting with Partial Geometric Priors
Rui Song, Tianhui Cai, Markus Gross +5
3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent wo…
SpanVLA: Learning from Negative-Recovery Samples with Fast Action Bridging for Vision-Language-Action Model
Zewei Zhou, Ruining Yang, Xuewei Qi +9
Vision-Language-Action (VLA) models bring rich world knowledge and reasoning capabilities to autonomous driving. However, most driving VLAs are trained only on positive expert demo…
BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving
Seth Z. Zhao, Luobin Wang, Hongwei Ruan +13
Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer effectively in closed-loop (CL) deployment. In…
TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments
Zhiyu Huang, Yun Zhang, Johnson Liu +3
Robots in dynamic, human-centric environments must follow language instructions while maintaining real-time reactive control. Vision-language-action (VLA) models offer a promising…