8 papers
Simulator-Refined Diffusion for Radio-Frequency Inverse Design
Jinhao Liang, Jacob K. Christopher, Michael Frei +2
Diffusion models have shown potential in inverse design of printed circuit boards (PCBs), enabling the generation of layouts conditioned on target S-parameters. Despite this promis…
Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
Jinhao Liang, Sven Koenig, Ferdinando Fioretto
Decentralized multi-robot motion planning requires each robot to generate collision-free trajectories from local observations, without global sensing or reliable communication. How…
Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
Jinhao Liang, Yixuan Sun, Anirban Samaddar +2
Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specificat…
Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning
Jinhao Liang, Sven Koenig, Ferdinando Fioretto
Multi-Robot Motion Planning (MRMP) involves generating collision-free trajectories for multiple robots operating in a shared continuous workspace. While discrete multi-agent path f…
Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation
Jacob K. Christopher, Michael Cardei, Jinhao Liang +1
Despite the remarkable generative capabilities of diffusion models, their integration into safety-critical or scientifically rigorous applications remains hindered by the need to e…
Gen-DFL: Decision-Focused Generative Learning for Robust Decision Making
Prince Zizhuang Wang, Shuyi Chen, Jinhao Liang +2
Decision-focused learning (DFL) integrates predictive models with downstream optimization, directly training machine learning models to minimize decision errors. While DFL has been…
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
Jinhao Liang, Jacob K Christopher, Sven Koenig +1
Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the envi…
Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models
Jinhao Liang, Jacob K. Christopher, Sven Koenig +1
Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to go…