Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

• URL: http://arxiv.org/abs/2606.10040v2
• Date: Wed, 10 Jun 2026 06:52:36 GMT
• Title: Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
• Abstract summary: World-Action Models (WAMs)couple future visual prediction with action generation.<n>Most existing WAMs rely on future prediction, which incurs high inference latency and makes real-time robot deployment difficult.<n>We introduce Efficient-WAM, a World-Action Model that reduces the cost of future imagination while preserving its control benefit.
• Score: 45.6948544726412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. However, most existing WAMs rely on photorealistic future prediction, which incurs high inference latency and makes real-time robot deployment difficult. This motivates a more efficient WAM design that preserves the control benefits of future visual prediction while reducing its inference cost. We introduce Efficient-WAM, a World-Action Model that reduces the cost of future imagination while preserving its control benefit. Efficient-WAM improves inference efficiency via a compact video expert transferred from WAN-2.2-5B, token-sparse video latents, and asymmetric video-action denoising that allocates fewer sampling steps to video than to actions. Instead of optimizing the future branch for visual fidelity, Efficient-WAM treats future video prediction as a compact guidance signal for action generation. Comprehensive experiments on RoboTwin 2.0 and real-world manipulation tasks show that Efficient-WAM maintains strong action performance despite visibly coarse future predictions. While maintaining competitive control capabilities, our 1B-parameter model can reduce per-chunk latency to around 100 ms during physical deployment, achieving a 30x speedup over existing WAMs.

Related papers

• SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving [6.9856663722260235]
  We propose SV-WAM, a surround-view world-action model (WAM) that preserves full six-camera observations while maintaining efficient inference.<n>At the core of this design is an action-centered causal mask that prevents action tokens from attending to future-video tokens during joint action-video denoising.<n>Experiments on the closed-loop NAVSIMv2 benchmark and the open-loop nuScenes benchmark demonstrate that SV-WAM achieves state-of-the-art planning performance.
  arXiv  Detail & Related papers  (2026-09-03T09:47:51Z)
• Vid2WAM: Distilling Video Diffusion Priors into World Action Models [59.28345153395937]
  World Action Models (WAMs) improve robot policy learning by jointly modeling future visual dynamics and actions.<n>We propose Vid2WAM, an offline distillation framework that transfers visual diffusion priors from a large video foundation model into a compact WAM student.<n>We show that Vid2WAM improves novel-task generalization and data efficiency under limited expert demonstrations.
  arXiv  Detail & Related papers  (2026-08-09T08:02:49Z)
• SimWAM: A Simple World Action Model for End-to-End Autonomous Driving [66.93548079427477]
  We present SimWAM, a simple yet effective WAM that leverages future-video prediction as a training-time supervision signal.<n>An isolated attention mask keeps action prediction independent of future frames, allowing trajectory prediction without explicit future-frame generation at inference.<n>Our SimWAM achieves 91.5 PDMS on NAVSIM, surpasses state-of-the-art WAM-based planners with substantially lower latency, and transfers zero-shot to nuScenes.
  arXiv  Detail & Related papers  (2026-08-07T17:59:09Z)
• Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models [40.0682886050991]
  World Action Models (WAMs) improve robot manipulation by learning how the environment evolves beyond the current observation.<n>Joint-WAMs preserve future-aware representations during inference but incur prohibitive computation costs.<n>We show that inference-time future conditioning is critical for generalization under distribution shifts.
  arXiv  Detail & Related papers  (2026-08-05T03:18:58Z)
• GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch [66.7936140772745]
  We present GigaWorld-Policy-0.5, an action-centered WAM designed for more efficient robot control.<n>For efficient inference, GigaWorld-Policy-0.5 introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts.<n>Experiments and ablations show that GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control.
  arXiv  Detail & Related papers  (2026-07-15T15:46:42Z)
• Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation [80.18527639791074]
  World action models have shown great promise for autonomous driving and urban navigation.<n>Existing approaches suffer key limitations.<n>We propose Metis, an end-to-end WAM framework that decouples video generation and action prediction.
  arXiv  Detail & Related papers  (2026-06-14T15:40:49Z)
• LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies [31.88655699486955]
  We present LaWAM, a latent-action-conditioned Latent World Model (LaWM)<n>LaWAM exposes predictive dynamics to robot policies through compact latent visual subgoals instead of reconstructed future video.<n>LaWAM runs in 187 ms per action-chunk prediction and achieves up to 24x lower wall-clock latency than pixel-space WAMs.
  arXiv  Detail & Related papers  (2026-06-14T12:06:58Z)
• Light-WAM: Efficient World Action Models with State-Fusion Action Decoding [15.384126562001027]
  Light-WAM is a lightweight World Action Model for efficient robot manipulation.<n>It is built with a compact video backbone and performs future-video supervision in a downsampled latent space.<n>Experiments demonstrate that Light-WAM maintains strong performance on LIBERO and achieves usable multi-task performance on RoboTwin 2.0.
  arXiv  Detail & Related papers  (2026-06-06T15:58:12Z)
• GigaWorld-Policy: An Efficient Action-Centered World--Action Model [50.107640832046464]
  GigaWorld-Policy is an action-centered WAM that learns 2D pixel-action dynamics while enabling efficient action decoding, with optional video generation.<n> Experimental results on real-world robotic platforms show that GigaWorld-Policy runs 9x faster than the leading WAM baseline, Motus.
  arXiv  Detail & Related papers  (2026-03-18T00:52:02Z)
• FUTURE-VLA: Forecasting Unified Trajectories Under Real-time Execution [27.399648455932397]
  FUTURE-VLA is a unified architecture that reformulates long-horizon control and future forecasting as a monolithic sequence-generation task.<n> FUTURE-VLA achieves success rates of 99.2% on LIBERO, 75.4% on RoboTwin, and 78.0% on a real-world Piper platform.
  arXiv  Detail & Related papers  (2026-02-05T14:27:43Z)
• mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs [5.109732854501585]
  We introduce mimic-video, a novel Video-Action Model (VAM) that pairs a pretrained Internet-scale video model with a flow matching-based action decoder conditioned on its latent representations.<n>Our approach achieves state-of-the-art performance on simulated and real-world robotic manipulation tasks, improving sample efficiency by 10x and convergence speed by 2x compared to traditional VLA architectures.
  arXiv  Detail & Related papers  (2025-12-17T18:47:31Z)
• Physical Autoregressive Model for Robotic Manipulation without Action Pretraining [65.8971623698511]
  We build upon autoregressive video generation models to propose a Physical Autoregressive Model (PAR)<n>PAR leverages the world knowledge embedded in video pretraining to understand physical dynamics without requiring action pretraining.<n>Experiments on the ManiSkill benchmark show that PAR achieves a 100% success rate on the PushCube task.
  arXiv  Detail & Related papers  (2025-08-13T13:54:51Z)
• VidMan: Exploiting Implicit Dynamics from Video Diffusion Model for Effective Robot Manipulation [79.00294932026266]
  VidMan is a novel framework that employs a two-stage training mechanism to enhance stability and improve data utilization efficiency. Our framework outperforms state-of-the-art baseline model GR-1 on the CALVIN benchmark, achieving a 11.7% relative improvement, and demonstrates over 9% precision gains on the OXE small-scale dataset.
  arXiv  Detail & Related papers  (2024-11-14T03:13:26Z)
• Reinforcement Learning with Action-Free Pre-Training from Videos [95.25074614579646]
  We introduce a framework that learns representations useful for understanding the dynamics via generative pre-training on videos. Our framework significantly improves both final performances and sample-efficiency of vision-based reinforcement learning.
  arXiv  Detail & Related papers  (2022-03-25T19:44:09Z)

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.