The Rust programming language is an attractive choice for robotics and related fields, offering highly efficient and memory-safe code. However, a key limitation preventing its broader adoption in these domains is the lack of high-quality, well-supported Automatic Differentiation (AD)—a fundamental technique that enables convenient derivative computation by systematically accumulating data during function evaluation. In this work, we introduce ad-trait, a new Rust-based AD library. Our implementation overloads Rust’s standard floating-point type with a flexible trait that can efficiently accumulate necessary information for derivative computation. The library supports both forward-mode and reverse-mode automatic differentiation, making it the first operator-overloading AD implementation in Rust to offer both options. Additionally, ad-trait leverages Rust’s performance-oriented features, such as Single Instruction, Multiple Data acceleration in forward-mode AD, to enhance efficiency. Through benchmarking experiments, we show that our library is among the fastest AD implementations across several programming languages for computing derivatives. Moreover, it is already integrated into a Rust-based robotics library, where we showcase its ability to facilitate fast optimization procedures. We conclude with a discussion of the limitations and broader implications of our work.

Object rearrangement is a fundamental sub-task in accomplishing a great many physical tasks. As such, effectively executing rearrangement is an important skill for intelligent robots to master. In this study, we conduct the first algorithmic study on optimally solving the problem of Multi-layer Object Rearrangement on a Tabletop (MORT), in which one object may be relocated at a time, and an object can only be moved if other objects do not block its top surface. In addition, any intermediate structure during the reconfiguration process must be physically stable, i.e., it should stand without external support. To tackle the dual challenges of untangling the dependencies between objects and ensuring structural stability, we develop an algorithm that interleaves the computation of the optimal rearrangement plan and structural stability checking. Using a carefully constructed integer linear programming (ILP) model, our algorithm, Stability-Aware Rearrangement Programming (SARP), readily scales to optimally solve complex rearrangement problems of 3D structures with over 60 building blocks, with solution quality significantly outperforming natural greedy best-first approaches. Upon the publication of the manuscript source code and data will be available at https//github.com/arc-1/mort/.

Continuous integration (CI) has been a de facto standard for building industrial-strength software. Yet, there is little attention towards applying CI to the development of machine learning (ML) applications until the very recent effort on the theoretical side. In this paper, we take a step forward to bring the theory into practice.