Ergodic Control and Controlled Diffusion for Robot Learning: Review and Tutorial
Diffusion learning leverages the statistical mechanism of diffusion processes for learning, reasoning, and inferring complex distributions from data. Recent advances in diffusion learning have been transformative, with robot learning emerging as a key opportunity area, with applications spanning perception, control, and decision-making. At the same time, the statistical mechanism of diffusion processes can be controlled to shape the temporal evolution of the state distribution underlying robot trajectories, inducing ergodic behavior in robotic systems. The frameworks of controlled diffusion and ergodic control were developed around the same time as diffusion learning, and their theories a...
| Authors | Max Muchen Sun, Cem Bilaloglu, Ananya Rao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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