5 papers
LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction
Sangjin Han, Hoseong Jung, Jeongtae Her +2
Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizi…
FLAG: Flow Policy MaxEnt-RL by Latent Augmented Guidance
Sungha Kim, Gawon Lee, Jusuk Lee +3
Maximum entropy reinforcement learning (MaxEnt-RL) enables robust exploration, yet practical implementations often restrict policies to simple Gaussians. While recent approaches in…
DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation
Jusuk Lee, Seungjae Lee, Jonghun Shin +6
Robot manipulation critically depends on perception that preserves the action-relevant aspects of a scene. Yet most robot learning pipelines are built upon visual encoders pre-trai…
Temporal Action Representation Learning for Tactical Resource Control and Subsequent Maneuver Generation
Hoseong Jung, Sungil Son, Daesol Cho +3
Autonomous robotic systems should reason about resource control and its impact on subsequent maneuvers, especially when operating with limited energy budgets or restricted sensing.…
Performance Plateaus in Inference-Time Scaling for Text-to-Image Diffusion Without External Models
Changhyun Choi, Sungha Kim, H. Jin Kim
Recently, it has been shown that investing computing resources in searching for good initial noise for a text-to-image diffusion model helps improve performance. However, previous…