ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment (2503.02505v1)

Abstract: We aim to develop a goal specification method that is semantically clear, spatially sensitive, and intuitive for human users to guide agent interactions in embodied environments. Specifically, we propose a novel cross-view goal alignment framework that allows users to specify target objects using segmentation masks from their own camera views rather than the agent's observations. We highlight that behavior cloning alone fails to align the agent's behavior with human intent when the human and agent camera views differ significantly. To address this, we introduce two auxiliary objectives: cross-view consistency loss and target visibility loss, which explicitly enhance the agent's spatial reasoning ability. According to this, we develop ROCKET-2, a state-of-the-art agent trained in Minecraft, achieving an improvement in the efficiency of inference 3x to 6x. We show ROCKET-2 can directly interpret goals from human camera views for the first time, paving the way for better human-agent interaction.

Cross-View Goal Alignment for Enhanced Human-Agent Interaction in Embodied Environments

The paper, "ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment," presents a promising framework aimed at improving human-agent interaction within embodied environments, particularly focusing on bridging the gap between human intent and agent action through innovative goal specification techniques.

Methodological Advancement

The authors introduce an intuitive cross-view goal alignment framework, enabling users to provide directives using segmentation masks from their personal camera views, rather than those observed by the agent. This method alleviates the misalignment seen in behavior cloning when there is a significant disparity between human and agent camera views. To support this, the paper introduces two auxiliary objectives: the cross-view consistency loss and the target visibility loss. These objectives are meticulously crafted to bolster the spatial reasoning capabilities of the agent, ensuring alignment of the agent's actions with human intentions.

The developed model, ROCKET-2, is trained within the Minecraft environment, showcasing a noteworthy improvement in inference efficiency, achieving speed increases of 3x to 6x. This enhancement underscores the practicality of the method in environments that demand real-time processing and decision-making.

Implications and Results

The primary implications of this research are twofold. On a practical level, the ability of ROCKET-2 to interpret goals from human camera views enhances its applicability in diverse, dynamic environments like virtual players and robotics. Theoretically, the paper contributes to the ongoing discourse on spatial reasoning in AI agents, offering a robust approach to maintaining consistent goal tracking despite occlusions or varying camera perspectives.

Additionally, experimental results illustrate that ROCKET-2 not only improves efficiency but also aligns with human intent more effectively than previous models. This is particularly evident in complex, partially observable 3D worlds where traditional goal-specification methods falter due to real-time goal generation requirements and occlusion challenges.

Future Directions

Looking forward, the methodologies presented have potential ramifications for advancing AI robustness and generalization. Future work could explore the adaptation of these principles to other domains of embodied AI, perhaps in more constrained or hazard-prone settings, where enhanced human-agent interaction is critical. The framework could also be adapted to support other forms of human input besides segmentation masks, potentially exploring natural language processing or combined multi-modal inputs for richer interaction dynamics.

Conclusion

"ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment" clearly marks a significant contribution to the field of AI-driven human-agent interaction, particularly in dynamically complex environments. By addressing cross-view goal specification divisively, the research offers clear benefits in terms of agent performance and interaction fluidity, paving the way for more nuanced, responsive AI systems in the not-so-distant future.