1 citations · 1 across the 12 of their papers we have counts for
12 papers
Sensitivity Shaping for Latent Modeling
Hongzhan Yu, Chenghao Li, Ruipeng Zhang +2
Generative dynamics models enable planning in challenging systems, but safe deployment requires detecting policy-induced out-of-distribution (OOD) transitions. Existing methods typ…
Learning to Nudge: A Scalable Barrier Function Framework for Safe Robot Interaction in Dense Clutter
Haixin Jin, Nikhil Uday Shinde, Soofiyan Atar +5
Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional…
GLIDE: A Coordinated Aerial-Ground Framework for Search and Rescue in Unknown Environments
Seth Farrell, Chenghao Li, Henrik I. Christensen
We present a cooperative aerial-ground search-and-rescue (SAR) framework that pairs two unmanned aerial vehicles (UAVs) with an unmanned ground vehicle (UGV) to achieve rapid victi…
May I have your Attention? Breaking Fine-Tuning based Prompt Injection Defenses using Architecture-Aware Attacks
Nishit V. Pandya, Andrey Labunets, Sicun Gao +1
A popular class of defenses against prompt injection attacks on large language models (LLMs) relies on fine-tuning to separate instructions and data, so that the LLM does not follo…
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…
Improving Compositional Generation with Diffusion Models Using Lift Scores
Chenning Yu, Sicun Gao
We introduce a novel resampling criterion using lift scores, for improving compositional generation in diffusion models. By leveraging the lift scores, we evaluate whether generate…
Safe Human Robot Navigation in Warehouse Scenario
Seth Farrell, Chenghao Li, Hongzhan Yu +3
The integration of autonomous mobile robots (AMRs) in industrial environments, particularly warehouses, has revolutionized logistics and operational efficiency. However, ensuring t…
Controllable Motion Generation via Diffusion Modal Coupling
Luobin Wang, Hongzhan Yu, Chenning Yu +2
Diffusion models have recently gained significant attention in robotics due to their ability to generate multi-modal distributions of system states and behaviors. However, a key ch…
Estimating Control Barriers from Offline Data
Hongzhan Yu, Seth Farrell, Ryo Yoshimitsu +3
Learning-based methods for constructing control barrier functions (CBFs) are gaining popularity for ensuring safe robot control. A major limitation of existing methods is their rel…
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
Chung-En Sun, Sicun Gao, Tsui-Wei Weng
Robustness remains a paramount concern in deep reinforcement learning (DRL), with randomized smoothing emerging as a key technique for enhancing this attribute. However, a notable…
Activation-Descent Regularization for Input Optimization of ReLU Networks
Hongzhan Yu, Sicun Gao
We present a new approach for input optimization of ReLU networks that explicitly takes into account the effect of changes in activation patterns. We analyze local optimization ste…
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh +3
Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficu…