Search papers, labs, and topics across Lattice.
Riemann-1.0 is a novel World Action Model that integrates multi-view visual observations, robot states, and action-specific dynamics into a single causal autoregressive framework, allowing for simultaneous policy execution and world simulation. This model leverages a progressive embodied pretraining approach, synthesizing learning from diverse sources such as egocentric human videos and robot trajectories, to enhance its manipulation capabilities. Achieving state-of-the-art results across various benchmarks, Riemann-1.0 demonstrates a significant improvement in long-horizon manipulation tasks, with success rates surpassing previous methods by notable margins.
Riemann-1.0 transforms embodied intelligence by unifying robot policy execution and world simulation, achieving unprecedented success rates in real-world manipulation tasks.
We introduce Riemann-1.0, a fully causal autoregressive World Action Model for embodied intelligence. Riemann-1.0 jointly models multi-view visual observations, robot states, and embodiment-specific actions within a unified causal autoregressive sequence, representing robot actions and world evolution as causal state transitions. Unlike existing WAMs based on joint generation, video-first prediction, or decoupled modeling paradigms, Riemann-1.0 unifies online robot policy execution and action-conditioned world simulation within a single model, enabling it to function as both an executable robot policy and a multi-embodiment visual world simulator. To scale embodied experience across heterogeneous data sources, we further develop a progressive embodied pretraining framework that unifies learning from egocentric human videos, handheld-gripper demonstrations, and heterogeneous robot trajectories under a shared World Action Modeling objective. Built upon 200K+ hours of interaction data, Riemann-1.0 progressively transfers large-scale embodied experience into executable robot manipulation capabilities. Riemann-1.0 achieves state-of-the-art performance across both simulation benchmarks and real-world manipulation tasks. It achieves success rates of 94.3% on RoboTwin2.0, 99.0% on LIBERO, and 62.6% on the long-horizon compositional benchmark RoboCasa-365, outperforming the previous best method by 8.4% On long-horizon real-world manipulation tasks, Riemann-1.0 achieves a Success Rate (SR) of 85.0% and a Progress Success Rate (PSR) of 94.4%, exceeding the strongest open-source baseline by 15% in SR. These results demonstrate that unified World Action Modeling together with progressive embodied pretraining effectively transforms large-scale embodied experience into generalizable robot manipulation capabilities.
Tsinghua AI