Journal

We present a framework called Selective Myopic bEhavior Control~(SMEC), which results from the insight that the short-term behaviors of prior policies are sharable across tasks.

We propose a novel framework that enables humanoid robots to traverse complex terrains with controllable human-like gaits using a mixture of latent residual experts and multi-discriminators.

We propose DVDF, a method for cross-domain offline RL that filters source data by both dynamics and value alignment, achieving strong performance in challenging settings.

We propose TACO, a test-time-scaling framework for VLAs that improves inference stability and success rates by preventing distribution shifts at test time.