action-conditioned

The paper presents SAAC-JEPA, a schema-adaptive action-conditioned JEPA model for transferring predictive representations across CNC machines with partial sensor overlap, demonstrating that cross-machine adaptation requires distinct evaluation beyond source-domain accuracy.

SyncWorld is an action-conditioned world model that uses visual calibration episodes to learn environment-specific action-to-visual mappings, enabling zero-shot simulation and test-time policy improvement across unseen robotic settings.

This paper investigates whether action-conditioned JEPA world models can perform latent planning with point cloud observations instead of images, finding that they succeed and discussing implications for geometric perception in robotics.

ForgeWM is a progressive framework that distills bidirectional video generators into efficient few-step interactive world models, supporting low-latency interaction and replay-time refinement with demonstrated improvements on Minecraft and FPS gameplay.

This paper proposes a minimal 'advantage-style' action channel for latent world models that cancels action-independent distractor variation by subtracting the mean effect over actions, improving controllability without auxiliary losses or reconstruction.

This paper proposes Music-JEPA, a world model that learns piano sound representations by framing audio as a state and piano roll as an action. It captures action-sound relationships and enables downstream tasks like beat tracking and piano transcription via planning.

Introduces DWM, a framework that decomposes latent world model transitions into action-driven and action-invariant (world effect) components, improving planning success on benchmarks with persistent world effects.

ABot-World-0 is a real-time, long-horizon interactive video world model that runs on a single desktop GPU, enabling infinite world rollout with action-conditioned control.

This paper introduces A2World, a diffusion-based world model pretrained on large-scale robot manipulation data to learn transferable dynamics priors. The model can be adapted into a real-world simulator (A2World-sim) for policy evaluation or a video-action prediction model (A2World-policy) for action prediction, demonstrating benefits for both simulator-centric and policy-centric robot learning.

Foresight is a failure detection framework for long-horizon robotic manipulation that uses action-conditioned world model latents and functional conformal prediction to monitor trajectories, trained only with final task labels. It demonstrates state-of-the-art performance across simulation and real robot tasks.

Echo-Memory presents a controlled study of memory mechanisms in action-conditioned world models, revealing that memory structure and capacity significantly impact open-domain return performance beyond replay fidelity. The study introduces a matched evaluation protocol and finds that raw context and state-space recurrence are strong mechanisms.

MiraBench is a hierarchical benchmark for evaluating action-conditioned reliability in robotic world models, assessing physics adherence, action-following fidelity, and optimism bias across 12 model configurations.

MultiWorld is a unified framework for multi-agent multi-view video world modeling that achieves accurate control of multiple agents while maintaining multi-view consistency through a Multi-Agent Condition Module and Global State Encoder.