7 papers
Sensitivity Shaping for Latent Modeling
Hongzhan Yu, Chenghao Li, Ruipeng Zhang +2
Generative dynamics models enable planning in challenging robotic systems, but safe deployment requires reliably detecting policy-induced out-of-distribution (OOD) transitions. Exi…
P-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization
Ruipeng Zhang, Zhihao Li, Haozhang Yuan +2
Hallucination has recently garnered significant research attention in Large Vision-Language Models (LVLMs). Direct Preference Optimization (DPO) aims to learn directly from the cor…
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation
Ruipeng Zhang, Zhihao Li, C. L. Philip Chen +1
Large vision language models (LVLMs) have made rapid advancements and are deployed across various applications, yet hallucinations remain a major challenge. Activation steering is…
Learning Quadruped Walking from Seconds of Demonstration
Ruipeng Zhang, Hongzhan Yu, Ya-Chien Chang +3
Quadruped locomotion provides a natural setting for understanding when model-free learning can outperform model-based control design, by exploiting data patterns to bypass the diff…
Sequence Modeling for Time-Optimal Quadrotor Trajectory Optimization with Sampling-based Robustness Analysis
Katherine Mao, Hongzhan Yu, Ruipeng Zhang +4
Time-optimal trajectories drive quadrotors to their dynamic limits, but computing such trajectories involves solving non-convex problems via iterative nonlinear optimization, makin…
When Maximum Entropy Misleads Policy Optimization
Ruipeng Zhang, Ya-Chien Chang, Sicun Gao
The Maximum Entropy Reinforcement Learning (MaxEnt RL) framework is a leading approach for achieving efficient learning and robust performance across many RL tasks. However, MaxEnt…