Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion

arXiv:2609.31439 · cs.RO · Submitted 2026-09-25 · Read on arXiv

cs.RO

Submitted: 2026-09-25

Updated: 2026-09-25

Project page: https://rtnow.github.io/leco

Terminology

• Ablation — 13× in this paper · explained in 1 episode
• Reinforcement Learning — 12× in this paper · explained in 4 episodes
• Cooperation — 7× in this paper · explained in 2 episodes
• Actor — 5× in this paper · explained in 3 episodes
• Admittance Control — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes

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