4 citations · 8 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 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…
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…
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…
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…
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Christopher E. Mower, Yuhui Wan, Hongzhan Yu +20
We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our sys…
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…
Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance
Hongzhan Yu, Chiaki Hirayama, Chenning Yu +2
There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and th…
Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation
Chenning Yu, Hongzhan Yu, Sicun Gao
Deep reinforcement learning in continuous domains focuses on learning control policies that map states to distributions over actions that ideally concentrate on the optimal choices…