FLIP: Flowability-Informed Powder Weighing

Autonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow, coupled with variability in laboratory conditions necessitates adaptive automation. This work introduces FLIP, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. Our key contribution lies in using material flowability, quantified by the angle of repose, to optimise physics-based simulations through Bayesian inference. This yields material-specific simulation environments capable of generating accurate training data, which reflects diverse powder behaviours, for training "robot chemists". Building on this, FLIP integrates quantified flowability into a curriculum learning strategy, fostering efficient acquisition of robust robotic policies by gradually introducing more challenging, less flowable powders. We validate the efficacy of our method on a robotic powder weighing task under real-world laboratory conditions. Experimental results show that FLIP with a curriculum strategy achieves a low dispensing error of 2.12 +/- 1.53 mg, outperforming methods that do not leverage flowability data, such as domain randomisation (6.11 +/- 3.92 mg). These results demonstrate FLIP's improved ability to generalise to previously unseen, more cohesive powders and to new target masses.

A Framework for Reproducible Benchmarking and Performance Diagnosis of SLAM Systems

We propose SLAMFuse, an open-source SLAM benchmarking framework that provides consistent crossplatform environments for evaluating multi-modal SLAM algorithms, along with tools for data fuzzing, failure detection, and diagnosis across different datasets. Our framework introduces a fuzzing mechanism to test the resilience of SLAM algorithms against dataset perturbations. This enables the assessment of pose estimation accuracy under varying conditions and identifies critical perturbation thresholds. SLAMFuse improves diagnostics with failure detection and analysis tools, examining algorithm behaviour against dataset characteristics. SLAMFuse uses Docker to ensure reproducible testing conditions across diverse datasets and systems by streamlining dependency management. Emphasizing the importance of reproducibility and introducing advanced tools for algorithm evaluation and performance diagnosis, our work sets a new precedent for reliable benchmarking of SLAM systems. We provide ready-to-use docker compatible versions of the algorithms and datasets used in the experiments, together with guidelines for integrating and benchmarking new algorithms. Code is available at https://github.com/nikolaradulov/slamfuse