We present a novel task planner - TaskNet for an autonomous excavator based on a data-driven method, which plans feasible task-level sequence by learning from demonstration data. Given a high-level excavation objective, our TaskNet planner can decompose it into sub-tasks, each of which can be further decomposed into task primitives with specifications. We train our TaskNet using an excavation trace generator and evaluate its performance using a 3D physically-based terrain and excavator simulator. As compared to imitation learning-based methods, the experimental results show that TaskNet can effectively learn task decomposition strategies. The resulting sequences of task primitives can be used as inputs by any excavator motion planner for generating feasible joint-level trajectories. We further validate TaskNet on a state-of-the-art autonomous excavator hardware and software system. The 49-ton autonomous excavator can successfully perform material loading tasks.

The clustering of autonomous driving scenario data can substantially benefit the autonomous driving validation and simulation systems by improving the simulation tests' completeness and fidelity. This article proposes a comprehensive data clustering framework for a large set of vehicle driving data. Existing algorithms utilize handcrafted features whose quality relies on the judgments of human experts. Additionally, the related feature compression methods are not scalable for a large dataset. Our approach thoroughly considers the traffic elements, including both in-traffic agent objects and map information. Meanwhile, we proposed a self-supervised deep learning approach for spatial and temporal feature extraction to avoid biased data representation. With the newly designed driving data clustering evaluation metrics based on data-augmentation, the accuracy assessment does not require a human-labeled dataset, which is subject to human bias. Via such unprejudiced evaluation metrics, we have shown our approach surpasses the existing methods that rely on handcrafted feature extractions.

MOTIVATION: Large metabolic models, including genome-scale metabolic models, are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualization and interactive exploration can facilitate a better understanding of these models. RESULTS: We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyze a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualized. From this overview, detailed subviews may be constructed and visualized in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realized as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case and discuss the strengths and weaknesses of different decomposition methods. AVAILABILITY AND IMPLEMENTATION: The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme.