Abstract: This letter presents a Differential Dynamic Programming (DDP) framework for trajectory optimization (TO) of hybrid systems with state-based switching. The proposed Hybrid Systems DDP (HS-DDP ...

Documentation is available via ReadTheDocs. Note: Please consider to use python-dtw package which is compatible with dtw for R. Sakoe, H.; Chiba, S., Dynamic programming algorithm optimization for ...

In 1958, McCarthy specified LISP, the second-oldest high-level programming language in widespread use today (only Fortran is older by one year). The full form of Lisp is List Processing. Like Fortran, ...

To fix the way we test and measure models, AI is learning tricks from social science. It’s not easy being one of Silicon Valley’s favorite benchmarks. SWE-Bench (pronounced “swee bench”) launched in ...

STreeD is a framework for optimal binary decision trees with separable optimization tasks. A separable optimization task is a task that can be optimized separately for the left and right subtree. The ...

This course will provide you basic Understanding of principles behind machine learning problems and will help implement and organize machine learning projects.

For decades, coders wrote critical systems in C and C++. Now they turn to Rust. Many software projects emerge because—somewhere out there—a programmer had a personal problem to solve. That’s more or ...

Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these ...

In recent years, it is a trend to integrate the ideas in game theory into the research of multi-robot system. In this paper, a team-competition model is proposed to solve a dynamic multi-robot task ...

Abstract: Recent work has shed light on the often unreliable performance of constrained least-squares estimation methods for robot mass-inertial parameter identification, particularly for high ...