Riemann-1.0: An Embodied World Action Model for Physical AI

• URL: http://arxiv.org/abs/2608.27033v1
• Date: Thu, 27 Aug 2026 12:21:26 GMT
• Title: Riemann-1.0: An Embodied World Action Model for Physical AI
• Abstract summary: We introduce a fully causal autoregressive World Action Model for embodied intelligence.<n>Rickel-1.0 unifies online robot policy execution and action-conditioned world simulation within a single model.<n>We develop a progressive embodied pretraining framework that unifies learning from egocentric human videos, handheld-gripper demonstrations, and heterogeneous robot trajectories.
• Score: 17.136136265925682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence [22.0636262013758]
  Pelican-Sim 1.0 is a general world model simulator for embodied intelligence.<n>It predicts future observations from visual context and robot actions to support downstream learning and decision making.
  arXiv  Detail & Related papers  (2026-09-10T17:51:01Z)
• GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation [73.02743604295246]
  WMBench is a benchmark constructed from real-robot teleoperation data and matched policy rollouts.<n>We analyze 7 video world models, 4 action representation schemes, and over 324,000 simulated policy rollouts paired with real robot executions.<n>Our experiments deliver three core insights: evaluator quality is dominated by long-horizon, action-faithful rollout consistency rather than short-term visual realism.
  arXiv  Detail & Related papers  (2026-07-02T17:02:43Z)
• SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation [33.50539650112054]
  We present SIMPLE, a unified simulation testbed for humanoid policy learning and evaluation.<n> SIMPLE couples the accurate contact-rich dynamics of MuJoCo with the rendering of IsaacSim.<n>It provides a large-scale environment comprising 60 diverse whole-body tasks, 50 indoor scenes, and over 1,000 object assets.
  arXiv  Detail & Related papers  (2026-06-06T17:55:43Z)
• RLDX-1 Technical Report [74.70437517338186]
  We introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT)<n>Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier Vision-Language-Action models.
  arXiv  Detail & Related papers  (2026-05-05T01:40:15Z)
• Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization [38.20385682344082]
  We introduce Being-H0.5, a foundational Vision-Language-Action model designed for robust cross-embodiment generalization across diverse robotic platforms.<n>We present UniHand-2.0, the largest embodied pre-training recipe to date, comprising over 35,000 hours of multimodal data across 30 distinct robotic embodiments.<n>Being-H0.5 achieves state-of-the-art results on simulated benchmarks, such as LIBERO (98.9%) and RoboCasa (53.9%)
  arXiv  Detail & Related papers  (2026-01-19T12:20:38Z)
• InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation [77.07565723756119]
  We present InternVLA-A1, a vision-language-action model with dynamic predictive capabilities.<n>We pre-train these models on heterogeneous data sources over real-world robot data, synthetic simulation data, and human videos.<n>We evaluate InternVLA-A1 on 12 real-world robotic tasks and a simulation benchmark.
  arXiv  Detail & Related papers  (2026-01-05T18:54:29Z)
• MiVLA: Towards Generalizable Vision-Language-Action Model with Human-Robot Mutual Imitation Pre-training [102.850162490626]
  We propose MiVLA, a vision-language-action model empowered by human-robot mutual imitation pre-training.<n>We show that MiVLA achieves strong improved generalization capability, outperforming state-of-the-art VLAs.
  arXiv  Detail & Related papers  (2025-12-17T12:59:41Z)
• Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer [59.02729900344616]
  GPU-accelerated, photorealistic simulation has opened a scalable data-generation path for robot learning.<n>We develop a teacher-student-bootstrap learning framework for vision-based humanoid loco-manipulation.<n>This represents the first humanoid sim-to-real policy capable of diverse articulated loco-manipulation using pure RGB perception.
  arXiv  Detail & Related papers  (2025-11-30T20:07:13Z)
• ProcTHOR: Large-Scale Embodied AI Using Procedural Generation [55.485985317538194]
  ProcTHOR is a framework for procedural generation of Embodied AI environments. We demonstrate state-of-the-art results across 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation.
  arXiv  Detail & Related papers  (2022-06-14T17:09:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.