A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements

This study presents a physics-informed machine learning-based control method for nonlinear dynamic systems with highly noisy measurements. Existing data-driven control methods that use machine learning for system identification cannot effectively cope with highly noisy measurements, resulting in unstable control performance. To address this challenge, the present study extends current physics-informed machine learning capabilities for modeling nonlinear dynamics with control and integrates them into a model predictive control framework. To demonstrate the capability of the proposed method we test and validate with two noisy nonlinear dynamic systems: the chaotic Lorenz 3 system, and turning machine tool. Analysis of the results illustrate that the proposed method outperforms state-of-the-art benchmarks as measured by both modeling accuracy and control performance for nonlinear dynamic systems under high-noise conditions

A life cycle assessment of recycled polypropylene fibre in concrete footpaths

This study assesses the environmental impact of four alternatives for reinforcing 100 m² of concrete footpath (Functional Unit, FU) by using cradle to gate life cycle assessment (LCA), based on the Australian context. Specifically, the four options considered are a) producing steel reinforcing mesh (SRM), b)producing virgin polypropylene (PP) fibre, c) recycling industrial PP waste and d) recycling domestic PP waste. The FU yields 364 kg of SRM (in a) and 40 kg of PP fibres (in b, c and d), necessary to achieve the same degree of reinforcing in concrete. All the activities required to produce these materials are considered in the study, namely manufacturing and transportation, and also recycling and reprocessing in the case of industrial and domestic recycled PP waste fibres. These processes are individually analysed and quantified in terms of material consumption, water use, and emissions into the environment. This allows for the impacts from producing recycled fibres to be compared with those from producing virgin PP fibre and SRM, which are traditionally used. The LCA results show that industrial recycled PP fibre offers important environmental benefits over virgin PP fibre. Specifically, the industrial recycled PP fibrecan save 50% of CO₂ equivalent, 65% of PO₄ equivalent, 29% of water and 78% of oil equivalent, compared to the virgin PP fibre. When compared to the SRM, the industrial recycled PP fibre can save 93% of CO₂ equivalent, 97% of PO₄ equivalent, 99% of water and 91% of oil equivalent. The domestic recycled PP fibre also generates reduced environmental impacts compared to virgin PP fibre, except for higher consumption of water associated with the washing processes

Waypoint-Based Imitation Learning for Robotic Manipulation

While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC). Waypoints can help address this problem by reducing the horizon of the learning problem for BC, and thus, the errors compounded over time. However, waypoint labeling is underspecified, and requires additional human supervision. Can we generate waypoints automatically without any additional human supervision? Our key insight is that if a trajectory segment can be approximated by linear motion, the endpoints can be used as waypoints. We propose Automatic Waypoint Extraction (AWE) for imitation learning, a preprocessing module to decompose a demonstration into a minimal set of waypoints which when interpolated linearly can approximate the trajectory up to a specified error threshold. AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10. Videos and code are available at https://lucys0.github.io/awe/Comment: The first two authors contributed equall

Inversions in Split Trees and Conditional Galton-Watson Trees

We study I(T), the number of inversions in a tree T with its vertices labeled uniformly at random. We first show that the cumulants of I(T) have explicit formulas. Then we consider X_n, the normalized version of I(T_n), for a sequence of trees T_n. For fixed T_n\u27s, we prove a sufficient condition for X_n to converge in distribution. For T_n being split trees [Devroye, 1999], we show that X_n converges to the unique solution of a distributional equation. Finally, when T_n\u27s are conditional Galton-Watson trees, we show that X_n converges to a random variable defined in terms of Brownian excursions. Our results generalize and extend previous work by Panholzer and Seitz [Panholzer and Seitz, 2012]