Self-supervised multi-modal imitation learning under skewed trajectory demonstrations.
This work proposes a two-stage framework for multi-modal policy acquisition under trajectory distribution skew, and introduces a frequency-aware dynamic scaling factor that reweights mode-conditioned learning signals according to mini-batch mode frequencies, thereby increasing the contribution of underrepresented modes...