arXiv:2607.02403v1 Announce Type: cross Abstract: Decision-time planning with action-conditioned world models has become a popular paradigm for embodied control. However, the standard planning cost judges a candidate solely by how close its predicted terminal state lies to the goal, leaving the realizability of the intermediate transitions unchecked -- a predicted trajectory can look convincing while the environment rollout drifts away from it. In this paper, we propose ACID, a decision-time planning framework that introduces cycle action consistency: the action inferred backward from a predic
The proliferation of world models in embodied AI necessitates more robust planning mechanisms to bridge the gap between simulated trajectories and real-world execution. ACID addresses a critical limitation in current planning paradigms by focusing on action consistency.
Improving the reliability and robustness of planning in embodied AI will accelerate the deployment of autonomous systems in complex, real-world environments, transforming industries from logistics to personal assistance.
The focus on 'action consistency' via inverse dynamics introduces a more reliable method for agents to predict and execute trajectories, reducing sim-to-real gaps and enhancing the safety and effectiveness of autonomous operations.
- · Embodied AI developers
- · Robotics companies
- · Logistics and manufacturing automation
- · AI hardware manufacturers
- · Companies relying on less robust planning algorithms
- · Sectors unwilling to adopt advanced AI planning
More reliable autonomous agents capable of performing complex tasks in unpredictable environments.
Accelerated adoption of AI in physical industries, leading to increased productivity and new service offerings.
Ethical and regulatory discussions intensify as autonomous systems become pervasive and capable of more nuanced decision-making.
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Read at arXiv cs.AI