Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation

arXiv:2606.23625 · cs.RO · Submitted 2026-06-22 · Read on arXiv

cs.RO

Submitted: 2026-06-22

Updated: 2026-10-11

Terminology

• Active Perception — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 9 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Digital Twin — 1× in this paper · explained in 5 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes

Sources

• What Matters in Learning from Large-Scale Datasets for Robot Manipulation
• 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
• Equivariant Diffusion Policy
• Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation
• RVT-2: Learning Precise Manipulation from Few Demonstrations
• Robotic Control via Embodied Chain-of-Thought Reasoning
• Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
• Vision in Action: Learning Active Perception from Human Demonstrations
• Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop
• Transporter Networks: Rearranging the Visual World for Robotic Manipulation
• Deep Learning using Rectified Linear Units (ReLU)
• Adam: A Method for Stochastic Optimization

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
• ARC: A Reasoning Recipe for Robot Foundation Models
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies