MDOCRSS 2026
Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning
Generate feasible motion. Coordinate safely.
ResearchFrom feasible generation
From feasible generation
to adaptive interaction.
How do dynamics, constraints, geometry, and feedback shape useful robot behavior? Our research follows these questions across planning, coordination, and contact-rich control.
An evolving research programNew methods join a shared framework. The works below trace the current progression, each with its own task setting and evidence.
A connected research trajectoryWhat should guide
Explore MDOC
What should guide
generation?
Dynamics, constraints, geometry, and contact feedback. Each work opens the next question in making generated behavior useful for robots.
Can we generate dynamically feasible, collision-free motion without demonstration training, and use that planner to coordinate a team of robots?
A useful motion plan must obey the robot's dynamics and avoid collisions, even when many robots share the same space. MDOC brings these requirements inside model-based diffusion: candidate controls are corrected during rollout, and known dynamics turn them into executable state trajectories. The planner estimates its denoising direction from evaluated rollouts without learning from demonstrations. MDOC-CBS then uses the same low-level mechanism to resolve conflicts between robots.
Dynamics rolloutsCBF projectionMulti-robot coordination
Connected ideas, distinct problem settings. Each method has its own scope, evidence, and trade-offs.
Papers & methods
MD-COASIROS 2026
Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling
Balance exploration, feasibility, and compute.
2GOPreprint
Model-Based Geometry-Aware Generative Optimization for Constrained Locomotion Planning
Let constraint geometry guide the search.
MGAPreprint
Manifold Generative Annealing for Contact Constraint-aware Impedance Control with Reinforcement Learning Prior
Refine interaction through its physical response.