Summary of Skill Transfer and Discovery For Sim-to-real Learning: a Representation-based Viewpoint, by Haitong Ma et al.
Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint
by Haitong Ma, Zhaolin Ren, Bo Dai, Na Li
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the application of representation learning in robotics control for sim-to-real skill transfer and discovery. By drawing inspiration from spectral decomposition of Markov decision processes, the authors develop a method that can linearly represent state-action value functions induced by any policies, which can be regarded as skills. These skill representations are found to be transferable across arbitrary tasks with the same transition dynamics. To address the sim-to-real gap in dynamics, the authors propose a skill discovery algorithm that learns new skills from real-world data. This approach promotes the discovery of new skills by enforcing orthogonal constraints between learned and simulated skills, and then synthesizes policies using the enlarged skill sets. The methodology is demonstrated by transferring quadrotor controllers from simulators to Crazyflie 2.1 quadrotors, showing that a single simulator task can be used to learn skill representations that are transferred to multiple real-world tasks, including hovering, taking off, landing, and trajectory tracking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use machine learning to help robots do things in the real world that they learned to do in a simulation. The authors take inspiration from an idea about decomposing complex systems into simpler parts. They show that this can be used to represent skills that robots have learned, and these skills can be transferred to new situations. To make this work, the authors propose a way to learn new skills by looking at real-world data. This helps to bridge the gap between what happens in simulations and what happens in the real world. The authors test their method using quadcopters and show that it works well. |
Keywords
* Artificial intelligence * Machine learning * Representation learning * Tracking
