Learning Foresight without Explicit Trajectories for 3D Diffusion Policies
3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only knowing what motion is feasible now, but also anticipating where the interaction is heading. Existing policies largely leave such foresight to emerge implicitly from action learning. We introduce Movement Trend Guidance, a simple but effective way to provide this foresight without introducing an explicit plan. From a short observation history, the policy learns a compact latent representation of interaction evolution. During training, sparse future gripper states supervise this representation; at inference, only the latent is retained as fu...
| Authors | Zhongbo Zhang, Zaibin Zhang, Yifan Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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