Associate Professor, University of California, Los Angeles
3 papers at NeurIPS 2025
SplashNet adds Rolling Time Normalization, Aggressive Channel Masking, and a Split‑and‑Share bilateral encoder to sEMG typing, slashing the emg2qwerty baseline’s zero‑shot and fine‑tuned CERs by 31 % and 21 % respectively with half the parameters.
We propose an offline goal-conditioned RL algorithm that achieves state-of-the-art performance on complex, long-horizon tasks without needing hierarchical policies or generative subgoal models.
