CLaD：通过跨模态潜在动力学实现具有基础前瞻性的规划
CLaD: Planning with Grounded Foresight via Cross-Modal Latent Dynamics
AI总结：
CLaD通过不对称跨注意力机制联合建模本体和语义状态演化，利用自监督目标和EMA编码器防止表征崩溃，实现高成功率的长序列动作生成。
摘要：
机器人操作涉及由底层动作耦合的运动学和语义转换。然而，现有方法在语义空间或潜在空间中规划，未显式对齐跨模态转换。为此，我们提出CLaD框架，通过不对称跨注意力机制建模本体和语义状态在动作下的联合演化。CLaD通过自监督目标、EMA目标编码器和辅助重建损失预测基础潜在前瞻，防止表征崩溃并锚定预测到可观测状态。预测的前瞻通过观察进行调制，以条件化扩散策略生成动作。在LIBERO-LONG基准测试中，CLaD实现了94.7%的成功率，与大尺寸VLAs相比，参数显著更少。
英文摘要：
Robotic manipulation involves kinematic and semantic transitions that are inherently coupled via underlying actions. However, existing approaches plan within either semantic or latent space without explicitly aligning these cross-modal transitions. To address this, we propose CLaD, a framework that models how proprioceptive and semantic states jointly evolve under actions through asymmetric cross-attention that allows kinematic transitions to query semantic ones. CLaD predicts grounded latent foresights via self-supervised objectives with EMA target encoders and auxiliary reconstruction losses, preventing representation collapse while anchoring predictions to observable states. Predicted foresights are modulated with observations to condition a diffusion policy for action generation. On LIBERO-LONG benchmark, CLaD achieves 94.7\% success rate, competitive with large VLAs with significantly fewer parameters.
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