Fetch My Beer: Synthetic-to-real Hierarchical Policy for Smooth Pick-and-place
cs.RO
Submitted: 2026-09-16
Updated: 2026-09-16
Project page: https://fetch-my-beer.github.io
Terminology
Sources
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
- Learning Diffusion Policy from Primitive Skills for Robot Manipulation
- Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
- Qwen3-VL Technical Report
- DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation
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