Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation

arXiv:2610.09309 · cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.RO

Submitted: 2026-10-07

Updated: 2026-10-07

Project page: https://claire0730.github.io/executable-goals

Terminology

• Trace — 22× in this paper · explained in 2 episodes
• World Model — 19× in this paper · explained in 7 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 1× in this paper
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow
• $\mu_0$: A Scalable 3D Interaction-Trace World Model
• DINOv3

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving