CLaD: Planning with Grounded Foresight via Cross-Modal Latent Dynamics

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.

KGpose: Keypoint-Graph Driven End-to-End Multi-Object 6D Pose Estimation via Point-Wise Pose Voting

This letter presents KGpose, a novel end-to-end framework for 6D pose estimation of multiple objects. Our approach combines keypoint-based method with learnable pose regression through `keypoint-graph', which is a graph representation of the keypoints. KGpose first estimates 3D keypoints for each object using an attentional multi-modal feature fusion of RGB and point cloud features. These keypoints are estimated from each point of point cloud and converted into a graph representation. The network directly regresses 6D pose parameters for each point through a sequence of keypoint-graph embedding and local graph embedding which are designed with graph convolutions, followed by rotation and translation heads. The final pose for each object is selected from the candidates of point-wise predictions. The method achieves competitive results on the benchmark dataset, demonstrating the effectiveness of our model. KGpose enables multi-object pose estimation without requiring an extra localization step, offering a unified and efficient solution for understanding geometric contexts in complex scenes for robotic applications.