5 papers · 1 filter
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…