Search

Even though reinforcement-learning-based algorithms achieved superhuman performance in many domains, the field of robotics poses significant challenges as the state and action spaces are continuous, and the reward function is predominantly sparse. Furthermore, on many occasions, the agent is devoid of access to any form of demonstration. Inspired by human learning, in this work, we propose a method named highlight experience replay (HiER) that creates a secondary highlight replay buffer for the most relevant experiences. For the weights update, the transitions are sampled from both the standard and the highlight experience replay buffer. It can be applied with or without the techniques of hindsight experience replay (HER) and prioritized experience replay (PER). Our method significantly improves the performance of the state-of-the-art, validated on 8 tasks of three robotic benchmarks. Furthermore, to exploit the full potential of HiER, we propose HiER+ in which HiER is enhanced with an arbitrary data collection curriculum learning method. Our implementation, the qualitative results, and a video presentation are available on the project site: http://www.danielhorvath.eu/hier/., Comment: Published in IEEE Access

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data., Comment: Accpeted for publication in the proceedings of the 22nd IFAC World Congress

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different industrial applications. In this article, we propose a sim2real transfer learning method based on domain randomization for object detection with which labeled synthetic datasets of arbitrary size and object types can be automatically generated. Subsequently, a state-of-the-art convolutional neural network, YOLOv4, is trained to detect the different types of industrial objects. With the proposed domain randomization method, we could shrink the reality gap to a satisfactory level, achieving 86.32% and 97.38% mAP50 scores, respectively, in the case of zero-shot and one-shot transfers, on our manually annotated dataset containing 190 real images. Our solution fits for industrial use as the data generation process takes less than 0.5 s per image and the training lasts only around 12 h, on a GeForce RTX 2080 Ti GPU. Furthermore, it can reliably differentiate similar classes of objects by having access to only one real image for training. To our best knowledge, this is the only work thus far satisfying these constraints., Comment: Published in IEEE Transactions on Robotics (T-RO)

Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open. Most of the present-day approaches follow the so-called Generate-and-Validate approach, where first a candidate solution is being generated and after validated against an oracle. The latter, however, might not give a reliable result, because of the imperfections in such oracles; one of which is usually the test suite. Although (re-) running the test suite is right under one's nose, in real life applications the problem of over- and underfitting often occurs, resulting in inadequate patches. Efforts that have been made to tackle with this problem include patch filtering, test suite expansion, careful patch producing and many more. Most approaches to date use post-filtering relying either on test execution traces or make use of some similarity concept measured on the generated patches. Our goal is to investigate the nature of these similarity-based approaches. To do so, we trained a Doc2Vec model on an open-source JavaScript project and generated 465 patches for 10 bugs in it. These plausible patches alongside with the developer fix are then ranked based on their similarity to the original program. We analyzed these similarity lists and found that plain document embeddings may lead to misclassification - it fails to capture nuanced code semantics. Nevertheless, in some cases it also provided useful information, thus helping to better understand the area of Automated Program Repair., Comment: Paper accepted in APR2021 conference

Space debris larger than 1 cm can damage space instruments and impact Earth. The low-Earth orbits (at heights smaller than 2000 km) and orbits near the geostationary- Earth orbit (at 35786 km height) are especially endangered, because most satellites orbit at these latitudes. With current technology space debris smaller than 10 cm cannot be tracked. Smaller space debris burn up and evaporate in the atmosphere, but larger ones fall to the Earth's surface. For practical reasons it would be important to know the mass, composition, shape, velocity, direction of motion and impact time of space debris re-entering the atmosphere and falling to Earth. Since it is very difficult to measure these physical parameters, almost nothing is known about them. To partly fill this gap, we performed computer modelling with which we studied the celestial mechanics of spherical re-entry particles falling to Earth due to air drag.We determined the time, velocity and angle of impact as functions of the launch height, direction, speed and size of spherical re-entry particles. Our results can also be used for semi-spherical meteoroid particles of the interplanetary dust entering the Earth's atmosphere., Comment: 16 pages, 8 figures