6 papers
Track4Action: Distilling World-Centric 3D Tracker into Vision-Language-Action Policies
Chenyi Wang, Xinkai Wang, Bokai Lin +4
Action labels tell a vision-language-action (VLA) policy which robot commands to imitate, but not how those commands change the 3D world. The aligned demonstration clip contains th…
LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior
Xinkai Wang, Chenyi Wang, Yifu Xu +7
We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models reg…
Adversarial Trust Poisoning in Vehicular Collaborative Perception
Yutong Liu, Chenyi Wang, Ming F. Li +1
Collaborative perception (CP) enables connected and autonomous vehicles to share sensor data and jointly reason about their environment. To defend against adversaries that fabricat…
Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusion
Chenyi Wang, Ruoyu Song, Raymond Muller +5
Autonomous vehicles depend on online HD map construction to perceive lane boundaries, dividers, and pedestrian crossings -- safety-critical road elements that directly govern motio…
Physical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory
Chenyi Wang, Yanmao Man, Raymond Muller +4
Multi-Object Tracking (MOT) is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms hav…
CP-FREEZER: Latency Attacks against Vehicular Cooperative Perception
Chenyi Wang, Ruoyu Song, Raymond Muller +5
Cooperative perception (CP) enhances situational awareness of connected and autonomous vehicles by exchanging and combining messages from multiple agents. While prior work has expl…