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About this paper
Ziyi Xu, Cem Bilaloglu, Yiming Li, Sylvain Calinon
Researchers at EPFL and Idiap Research Institute developed an adaptive ergodic imitation approach for robots to robustly perform tasks in environments differing from demonstrations. The method continuously interpolates between strict trajectory tracking and adaptive exploration, allowing robots to detect task-progress mismatches and autonomously adjust their behavior to overcome obstacles, then return to tracking the nominal path.
Key insights
- Treating trajectory tracking as a specific instance of exploration within an ergodic control framework enables a unified and continuous spectrum of adaptive robot behaviors.
- Anisotropic diffusion, guided by demonstration geometry and task progress, provides a principled way to control the extent and direction of exploration while preserving task coherence.
- The Maximum Mean Discrepancy (MMD) metric, coupled with a receding horizon and inclusion of past trajectories, effectively guides the ergodic controller to cover novel areas efficiently and prevent redundant exploration.
Summary by alphaXiv.
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