Experience Selection Using Dynamics Similarity for Efficient Multi-Source Transfer Learning Between Robots
Using ν-Gap based dynamics similarity and Bayesian optimization, the proposed method efficiently selects source experiences, improving target robot performance by 62%.
Key Findings
Methodology
This paper introduces a dynamics similarity estimation framework inspired by the ν-Gap metric from robust control, applied to unknown robotic systems. The approach involves: • Measuring frequency domain responses of source and target robots via periodic excitation; • Fitting Gaussian Process models to response differences; • Defining a similarity metric ψ(ω) based on response amplitude and phase; • Using Bayesian optimization with Expected Improvement (EI) to find the maximum of ψ(ω) over a frequency range; • Selecting the source robot with minimal worst-case response difference for transfer. This method circumvents the need for detailed models, relying solely on simple frequency response data, making it adaptable and data-efficient.
Key Results
- In quadrotor experiments, selecting the most similar source robot via the proposed metric led to a 62% improvement in trajectory tracking performance, outperforming random or non-similarity-based choices.
- Simulations demonstrated that the algorithm converged within 7 iterations, accurately estimating the response difference with errors below 0.1, outperforming traditional frequency analysis.
- The approach effectively reduces negative transfer risks in multi-source settings, validating its practicality for real-world robotic applications.
Significance
This work addresses a critical bottleneck in robotic transfer learning: quantifying the similarity between unknown source and target systems efficiently and robustly. By leveraging frequency response measurements and Bayesian optimization, it provides a scalable, model-free solution that enhances transfer safety and effectiveness. Its applicability to UAVs, robotic arms, and multi-robot systems promises to accelerate autonomous learning, reduce training costs, and improve system robustness, thus advancing both academic research and industrial deployment.
Technical Contribution
The paper pioneers the integration of ν-Gap inspired metrics with Bayesian optimization for data-efficient similarity estimation in unknown robotic systems. It introduces a novel frequency domain response-based approach that avoids detailed system identification, enabling rapid source robot evaluation. The method guarantees a robust, scalable, and model-free process for source selection, significantly improving transfer learning efficiency and safety compared to existing feature-based or model-dependent techniques.
Novelty
This is the first application of the ν-Gap metric to robotic dynamics similarity assessment, combined with Bayesian optimization to efficiently estimate the worst-case response difference over a frequency range. Unlike traditional methods requiring explicit system models or extensive data, this approach relies solely on frequency response measurements, making it highly suitable for complex, unknown systems. Its innovative fusion of control theory and machine learning techniques marks a significant advancement in transfer learning methodology.
Limitations
- The current method assumes linear, minimum-phase systems and periodic excitation signals, which may limit applicability to highly nonlinear or non-minimum phase robots.
- Response measurement accuracy can be affected by noise, requiring robust filtering or noise-aware modeling.
- Computational complexity increases with the number of source robots, necessitating further optimization for large-scale multi-source scenarios.
Future Work
Future research will focus on extending the framework to nonlinear and time-varying systems, integrating deep learning models for richer response representation, and developing scalable algorithms for large multi-source environments. Additionally, exploring adaptive excitation strategies and real-time implementation will further enhance practical deployment in autonomous robotic systems.
AI Executive Summary
Transfer learning in robotics promises to significantly accelerate skill acquisition and improve performance by leveraging prior experience. However, a persistent challenge lies in accurately assessing the similarity between source and target systems, especially when their dynamics are unknown. Traditional approaches rely heavily on detailed models or extensive data collection, which are often impractical in real-world scenarios. This paper introduces a novel, data-efficient method inspired by the ν-Gap metric from robust control theory, designed to quantify the dynamics similarity between unknown robots using simple frequency response measurements.
The core innovation involves measuring the amplitude and phase response of source and target robots to periodic inputs, fitting Gaussian Process models to these responses, and employing Bayesian optimization to identify the worst-case response difference over a specified frequency range. This similarity metric guides the selection of the most appropriate source robot, ensuring positive transfer and minimizing negative effects. The approach is validated through quadrotor experiments, where the most similar source robot, identified via the proposed metric, enabled a 62% improvement in trajectory tracking performance.
Simulation results further confirm that the method converges rapidly within seven iterations, accurately estimating the response differences with minimal data. The practical significance of this work lies in its ability to facilitate safe, efficient, and model-free transfer learning across diverse robotic platforms, including UAVs and robotic arms. By enabling intelligent source selection, the method reduces training time, enhances robustness, and broadens the applicability of autonomous learning systems.
Looking ahead, future efforts will aim to extend this framework to handle nonlinear, time-varying systems, incorporate deep learning for richer response modeling, and optimize scalability for large multi-source environments. Overall, this research marks a substantial step toward more autonomous, adaptable, and intelligent robotic systems, with broad implications for industry and academia alike.
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房里准备一道菜，你有很多不同的锅和厨具，但你不知道哪个最适合做这道菜。于是你用不同的火力和时间试验，每次都观察菜的熟度和味道。经过几次尝试，你会发现某个锅用起来特别顺手，做出来的菜也更好吃。这个过程就像机器人在学习新技能时，试着用不同的经验源，找到最适合它的那一个。这样，机器人不用知道所有复杂的细节，只需要简单的测试，就能快速找到最合适的经验源，学得更快、更安全。
ELI14 Explained like you're 14
想象你在玩一个超级酷的游戏，你想让你的角色变得更厉害。你可以借用朋友的技能，但每个人的技能都不一样。有的朋友用起来特别顺手，效果也最好。你就会试着用他们的技能做任务，看看哪个效果最好。每次试完，你都记住哪个朋友的技能最适合你，然后用那个技能。这样，你就不用每次都从头学，而是直接用最合适的技能变得更厉害。这就像机器人在学习新任务时，选择最相似的经验源，帮助它更快、更安全地掌握技能。通过不断试验和比较，机器人可以节省时间，避免走弯路，变得更聪明、更安全。
Abstract
In the robotics literature, different knowledge transfer approaches have been proposed to leverage the experience from a source task or robot -- real or virtual -- to accelerate the learning process on a new task or robot. A commonly made but infrequently examined assumption is that incorporating experience from a source task or robot will be beneficial. In practice, inappropriate knowledge transfer can result in negative transfer or unsafe behaviour. In this work, inspired by a system gap metric from robust control theory, the $ν$-gap, we present a data-efficient algorithm for estimating the similarity between pairs of robot systems. In a multi-source inter-robot transfer learning setup, we show that this similarity metric allows us to predict relative transfer performance and thus informatively select experiences from a source robot before knowledge transfer. We demonstrate our approach with quadrotor experiments, where we transfer an inverse dynamics model from a real or virtual source quadrotor to enhance the tracking performance of a target quadrotor on arbitrary hand-drawn trajectories. We show that selecting experiences based on the proposed similarity metric effectively facilitates the learning of the target quadrotor, improving performance by 62% compared to a poorly selected experience.