核心概念
Efficiently improving robotic manipulation skills through the BOpt-GMM approach.
要約
The content discusses the challenges of sample-efficient learning in robotic manipulation and introduces the BOpt-GMM approach. It combines imitation learning with autonomous skill execution to enhance skill models using Bayesian optimization. The study demonstrates improved sample efficiency in complex manipulation tasks through simulations and real-world experiments.
I. Introduction
- Learning efficient manipulation motions remains a challenge.
- Behavioral Cloning (BC) is effective but requires many demonstrations.
- Dynamical systems offer sample efficiency but need updates based on environmental feedback.
II. Related Work
- Learning from human demonstrations has been successful in robotics.
- GMMs and DMPs enable learning from few demonstrations efficiently.
- Previous works have used BOpt for optimizing policies in manipulation tasks.
III. Problem Formulation
- Sparse reinforcement learning setting with policy optimization.
- Objective is to maximize reward accumulation over episodes.
- Surrogate model used to guide Bayesian Optimization process.
IV. BOpt-GMM Framework
- Utilizes gradient-free Bayesian Optimization for policy improvement.
- Encodes policy as GMM and updates means and covariances efficiently.
- Combines BOpt with GMM-based policy model for optimization.
V. Experimental Evaluation
- Evaluation conducted in simulated scenarios and real-world experiments.
- Comparison with baselines like SAC-GMM and Online-GMM.
- Results show improved sample efficiency and success rates with BOpt-GMM.
VI. Conclusion
- Proposed BOpt-GMM approach enhances sample efficiency in robotic manipulation tasks.
- Future work includes combining BOpt-GMM with SAC-GMM for further improvements.
統計
"Far more data efficient are the approaches that fit a parameterized model of the robotic skill from data."
"We demonstrated that our approach boosts the dynamical systems’ performance to 80 + % after around 500 episodes of autonomous exploration."
"We propose two effective, low-dimensional update methods for GMM encoded policies."
引用
"Efficient methods for learning new manipulation motions in a fast and reliable manner is still an open area of research in robotics."
"Our approach differs from the discussed works in three main points: 1) We do not assume the existence of predefined control primitives or motion models but learn these fully as reactive systems from demonstration data."