Jonathan DeCastro

Assistive agents in shared control must act on the human's intent while staying legible enough for the human to respond in real time. Dream2Assist adds intent inference to a recurrent state-space world model and trains the assistive policy through fictitious co-play, anticipating discrete human objectives without overriding human agency. Three human-subjects studies and a preliminary on-vehicle deployment span high-speed racing (N=30) and urban driving with richer intent structure (N=10), where intent conditioning yields measurable improvements in torque alignment and collision rate.

Assistive agents in shared control must act on the human's intent while staying legible enough for the human to respond in real time. Dream2Assist adds intent inference to a recurrent state-space world model and trains the assistive policy through fictitious co-play, anticipating discrete human objectives without overriding human agency. Three human-subjects studies and a preliminary on-vehicle deployment span high-speed racing (N=30) and urban driving with richer intent structure (N=10), where intent conditioning yields measurable improvements in torque alignment and collision rate.