Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

面向欠驱动双摆任务的平均回报最大熵强化学习

Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks

针对IROS 2024 AI Olympics竞赛中的欠驱动双摆任务，提出结合平均回报与最大熵的无模型RL算法AR-EAPO，在仿真场景的两类双摆任务中性能和鲁棒性均优于基线，无需复杂奖励设计或系统模型。

用于双摆周期轨道的变分延拓

Variational Continuation for Double Pendulum Periodic Orbits

本研究提出基于自动微分的海森矩阵变分延拓方法，用于数值延拓动力系统周期轨道，成功实现双摆周期轨道从不动点的延拓，发现了两摆质量不同时静止的周期轨道。

Characterizing information loss in a chaotic double pendulum with the Information Bottleneck

A hallmark of chaotic dynamics is the loss of information with time. Although information loss is often expressed through a connection to Lyapunov exponents -- valid in the limit of high information about the system state -- this picture misses the rich spectrum of information decay across different levels of granularity. Here we show how machine learning presents new opportunities for the study of information loss in chaotic dynamics, with a double pendulum serving as a model system. We use the Information Bottleneck as a training objective for a neural network to extract information from the…

ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias

Deep learning models are able to approximate one specific dynamical system but struggle at learning generalisable dynamics, where dynamical systems obey the same laws of physics but contain different numbers of elements (e.g., double- and triple-pendulum systems). To relieve this issue, we proposed the Modular Lagrangian Network (ModLaNet), a structural neural network framework with modularity and physical inductive bias. This framework models the energy of each element using modularity and then construct the target dynamical system via Lagrangian mechanics. Modularity is beneficial for reusin…

Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration

In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the features are identified from a high-level specification of the robot's morphology, consisting of the number and connectivity structure of its links. Model predictive control is then used to choose the actions under an optimistic model of the dynamics, which produces an efficient and goal-directed exp…

多保真度SINDy：基于保真度加权测量的非线性动力系统稀疏发现

Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements

针对异质噪声数据，提出多保真度SINDy方法，通过加权回归融合集成SINDy和弱SINDy，从不同保真度测量中稀疏识别非线性动力系统，理论证明加权策略的统计合理性，在常微分和偏微分方程基准系统及双摆预测中验证了其抑制异方差噪声、利用低成本低质量数据提升模型恢复的效果。