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Soft robots have high adaptability and safeness which are derived from their softness, and therefore it is paid attention to use them in human society. However, the controllability of soft robots is not enough to perform dexterous behaviors when considering soft robots as alternative laborers for humans. The model-based control is effective to achieve dexterous behaviors. When considering building a model which is suitable for control, there are problems based on their special properties such as the creep behavior or the variability of motion. In this paper, the lumped parameterized model with viscoelastic joints for a soft finger is established for the creep behavior. Parameters are expressed as distributions, which makes it possible to take into account the variability of motion. Furthermore, stochastic analyses are performed based on the parameters' distribution. They show high adaptivity compared with experimental results and also enable the investigation of the effects of parameters for robots' variability.

The popularity of data augmentation techniques in machine learning has increased in recent years, as they enable the creation of new samples from existing datasets. Rotational augmentation, in particular, has shown great promise by revolving images and utilising them as additional data points for training. This research study introduces a new approach to enhance the performance of classification methods where the testing sets were generated employing transformations on every image from the original dataset. Subsequently, ensemble-based systems were implemented to determine the most reliable outcome in each subset acquired from the augmentation phase to get a final prediction for every original image. The findings of this study suggest that rotational augmentation techniques can significantly improve the accuracy of standard classification models; and the selection of a voting scheme can considerably impact the model's performance. Overall, the study found that using an ensemble-based voting system produced more accurate results than simple voting.

With the increasing utilization of Internet of Things (IoT) enabled drones in diverse applications like photography, delivery, and surveillance, concerns regarding privacy and security have become more prominent. Drones have the ability to capture sensitive information, compromise privacy, and pose security risks. As a result, the demand for advanced technology to automate drone detection has become crucial. This paper presents a project on a transfer-based drone detection scheme, which forms an integral part of a computer vision-based module and leverages transfer learning to enhance performance. By harnessing the knowledge of pre-trained models from a related domain, transfer learning enables improved results even with limited training data. To evaluate the scheme's performance, we conducted tests on benchmark datasets, including the Drone-vs-Bird Dataset and the UAVDT dataset. Notably, the scheme's effectiveness is highlighted by its IOU-based validation results, demonstrating the potential of deep learning-based technology in automating drone detection in critical areas such as airports, military bases, and other high-security zones.

In sim-to-real Reinforcement Learning (RL), a policy is trained in a simulated environment and then deployed on the physical system. The main challenge of sim-to-real RL is to overcome the reality gap - the discrepancies between the real world and its simulated counterpart. Using general geometric representations, such as convex decomposition, triangular mesh, signed distance field can improve simulation fidelity, and thus potentially narrow the reality gap. Common to these approaches is that many contact points are generated for geometrically-complex objects, which slows down simulation and may cause numerical instability. Contact reduction methods address these issues by limiting the number of contact points, but the validity of these methods for sim-to-real RL has not been confirmed. In this paper, we present a contact reduction method with bounded stiffness to improve the simulation accuracy. Our experiments show that the proposed method critically enables training RL policy for a tight-clearance double pin insertion task and successfully deploying the policy on a rigid, position-controlled physical robot.

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings.

Falls among the elderly are a major health concern, frequently resulting in serious injuries and a reduced quality of life. In this paper, we propose "BlockTheFall," a wearable device-based fall detection framework which detects falls in real time by using sensor data from wearable devices. To accurately identify patterns and detect falls, the collected sensor data is analyzed using machine learning algorithms. To ensure data integrity and security, the framework stores and verifies fall event data using blockchain technology. The proposed framework aims to provide an efficient and dependable solution for fall detection with improved emergency response, and elderly individuals' overall well-being. Further experiments and evaluations are being carried out to validate the effectiveness and feasibility of the proposed framework, which has shown promising results in distinguishing genuine falls from simulated falls. By providing timely and accurate fall detection and response, this framework has the potential to substantially boost the quality of elderly care.

Automated program repair techniques aim to aid software developers with the challenging task of fixing bugs. In heuristic-based program repair, a search space of program variants is created by applying mutation operations on the source code to find potential patches for bugs. Most commonly, every selection of a mutation operator during search is performed uniformly at random. The inefficiency of this critical step in the search creates many variants that do not compile or break intended functionality, wasting considerable resources as a result. In this paper, we address this issue and propose a reinforcement learning-based approach to optimise the selection of mutation operators in heuristic-based program repair. Our solution is programming language, granularity-level, and search strategy agnostic and allows for easy augmentation into existing heuristic-based repair tools. We conduct extensive experimentation on four operator selection techniques, two reward types, two credit assignment strategies, two integration methods, and three sets of mutation operators using 22,300 independent repair attempts. We evaluate our approach on 353 real-world bugs from the Defects4J benchmark. Results show that the epsilon-greedy multi-armed bandit algorithm with average credit assignment is best for mutation operator selection. Our approach exhibits a 17.3% improvement upon the baseline, by generating patches for 9 additional bugs for a total of 61 patched bugs in the Defects4J benchmark.

Improving the generalization capabilities of general-purpose robotic agents has long been a significant challenge actively pursued by research communities. Existing approaches often rely on collecting large-scale real-world robotic data, such as the RT-1 dataset. However, these approaches typically suffer from low efficiency, limiting their capability in open-domain scenarios with new objects, and diverse backgrounds. In this paper, we propose a novel paradigm that effectively leverages language-grounded segmentation masks generated by state-of-the-art foundation models, to address a wide range of pick-and-place robot manipulation tasks in everyday scenarios. By integrating precise semantics and geometries conveyed from masks into our multi-view policy model, our approach can perceive accurate object poses and enable sample-efficient learning. Besides, such design facilitates effective generalization for grasping new objects with similar shapes observed during training. Our approach consists of two distinct steps. First, we introduce a series of foundation models to accurately ground natural language demands across multiple tasks. Second, we develop a Multi-modal Multi-view Policy Model that incorporates inputs such as RGB images, semantic masks, and robot proprioception states to jointly predict precise and executable robot actions. Extensive real-world experiments conducted on a Franka Emika robot arm validate the effectiveness of our proposed paradigm. Real-world demos are shown in YouTube (//www.youtube.com/watch?v=1m9wNzfp_4E ) and Bilibili (//www.bilibili.com/video/BV178411Z7H2/ ).

Since the rise of fair machine learning as a critical field of inquiry, many different notions on how to quantify and measure discrimination have been proposed in the literature. Some of these notions, however, were shown to be mutually incompatible. Such findings make it appear that numerous different kinds of fairness exist, thereby making a consensus on the appropriate measure of fairness harder to reach, hindering the applications of these tools in practice. In this paper, we investigate one of these key impossibility results that relates the notions of statistical and predictive parity. Specifically, we derive a new causal decomposition formula for the fairness measures associated with predictive parity, and obtain a novel insight into how this criterion is related to statistical parity through the legal doctrines of disparate treatment, disparate impact, and the notion of business necessity. Our results show that through a more careful causal analysis, the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions through the concept of business necessity. Finally, we demonstrate the importance of our findings on a real-world example.

The demand for artificial intelligence has grown significantly over the last decade and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, in order to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges, first and foremost the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.