21 citations · 40 across the 10 of their papers we have counts for
12 papers
Controllable and Photorealistic Pedestrian Risky Motion Generation for End-to-End Driving Safety Evaluation
Siyuan Liu, Miao Li, Haibao Yu +4
Evaluating end-to-end autonomous driving under rare, safety-critical vehicle-pedestrian interactions requires photorealistic, sensor-level scenarios. However, trajectory-based scen…
Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving
Haohong Lin, Yunzhi Zhang, Wenhao Ding +2
End-to-end (E2E) autonomous driving models have demonstrated strong performance in open-loop evaluations but often suffer from cascading errors and poor generalization in closed-lo…
Query-Centric Diffusion Policy for Generalizable Robotic Assembly
Ziyi Xu, Haohong Lin, Shiqi Liu +1
The robotic assembly task poses a key challenge in building generalist robots due to the intrinsic complexity of part interactions and the sensitivity to noise perturbations in con…
CrashAgent: Crash Scenario Generation via Multi-modal Reasoning
Miao Li, Wenhao Ding, Haohong Lin +4
Training and evaluating autonomous driving algorithms requires a diverse range of scenarios. However, most available datasets predominantly consist of normal driving behaviors demo…
Causal Composition Diffusion Model for Closed-loop Traffic Generation
Haohong Lin, Xin Huang, Tung Phan-Minh +6
Simulation is critical for safety evaluation in autonomous driving, particularly in capturing complex interactive behaviors. However, generating realistic and controllable traffic…
OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning
Yihang Yao, Zhepeng Cen, Wenhao Ding +5
Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using a pre-collected dataset. Most current methods struggle with the mismatch between im…
BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
Haohong Lin, Wenhao Ding, Jian Chen +4
Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies, especially in scenarios where explorati…
Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assembly
Haohong Lin, Radu Corcodel, Ding Zhao
Furniture assembly remains an unsolved problem in robotic manipulation due to its long task horizon and nongeneralizable operations plan. This paper presents the Tactile Ensemble S…
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
Haohong Lin, Wenhao Ding, Zuxin Liu +4
In the domain of autonomous driving, the offline Reinforcement Learning~(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline data…
A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective
Wenhao Ding, Chejian Xu, Mansur Arief +3
Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms.…
CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation
Wenhao Ding, Haohong Lin, Bo Li +1
Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems. However, the d…
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance
Wenhao Ding, Haohong Lin, Bo Li +1
Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness. Extending purely data-driven generat…