--ff0459e4·2 events·first seen Aliases: π₀.₅, π₀
DLAM introduces a distributional latent-action model that represents robot action transitions as diagonal Gaussians, enabling structured extraction of action priors from action-free video data. The approach uses normalized composition and reversal over equal-gap triplets to constrain both mean and variance, addressing error propagation in recursive composition that affects prior deterministic methods. A flow-matching policy jointly generates mean transition sequences and robot actions, and the method outperforms latent-action baselines on MetaWorld MT50, LIBERO, and real-world manipulation tasks under a controlled π₀ transfer protocol.
DexHoldem is a new system-level benchmark for evaluating dexterous embodied agents on a ShadowHand robot performing Texas Hold'em card manipulation tasks. It provides 1,470 teleoperated demonstrations across 14 manipulation primitives, a physical policy benchmark, and an agentic perception benchmark for structured game-state recovery. Top performers include π₀.₅ at 61.2% task completion and Claude Opus 4.7 at 34.3% strict perception accuracy, with GPT 5.5 achieving 66.8% field-wise accuracy. The benchmark exposes gaps between isolated visual sub-capabilities and full closed-loop embodied decision-making.