Self-supervised cross-modality learning for uncertainty-aware object detection and recognition in applications which lack pre-labelled training data

This paper shows how an uncertainty-aware, deep neural network can be trained to detect, recognise and localise objects in 2D RGB images, in applications lacking annotated train-ng datasets. We propose a self-supervising teacher-student pipeline, in which a relatively simple teacher classifier, trained with only a few labelled 2D thumbnails, automatically processes a larger body of unlabelled RGB-D data to teach a student network based on a modified YOLOv3 architecture. Firstly, 3D object detection with back projection is used to automatically extract and teach 2D detection and localisation information to the student network. Secondly, a weakly supervised 2D thumbnail classifier, with minimal training on a small number of hand-labelled images, is used to teach object category recognition. Thirdly, we use a Gaussian Process GP to encode and teach a robust uncertainty estimation functionality, so that the student can output confidence scores with each categorization. The resulting student significantly outperforms the same YOLO architecture trained directly on the same amount of labelled data. Our GP-based approach yields robust and meaningful uncertainty estimations for complex industrial object classifications. The end-to-end network is also capable of real-time processing, needed for robotics applications. Our method can be applied to many important industrial tasks, where labelled datasets are typically unavailable. In this paper, we demonstrate an example of detection, localisation, and object category recognition of nuclear mixed-waste materials in highly cluttered and unstructured scenes. This is critical for robotic sorting and handling of legacy nuclear waste, which poses complex environmental remediation challenges in many nuclearised nations.

An Adaptive Dual-Domain Prediction Strategy based on Second-order Derivatives for Dynamic Multi-Objective Optimization

This paper addresses the problem of dynamic multi-objective optimization problems (DMOPs), by demonstrating new approaches to change prediction strategies within an evolutionary algorithm paradigm. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic. To accurately track the changing PS and PF in the decision and objective spaces, we propose a novel adaptive prediction strategy, which utilizes the concept of second-order derivatives adaptively in different domains. %to deal with DMOPs. Firstly, the changes in both the PS and the PF are considered in this paper, which makes the proposed a dual-domain based method. Firstly, we propose a dual-domain method, which takes into account changes in both the PS and the PF simultaneously. An adaptive strategy is adopted to self-adjust the proportion of the search space. Secondly, a second-order derivative prediction strategy is proposed to predicatively re-initialize the population. We compare the performance of the proposed algorithm against four other state-of-the-art algorithms from the literature, using DMOPs benchmark problems. Experimental results show that the proposed method outperforms the other algorithms on most of the test problems.

A Mini-Review on Mobile Manipulators with Variable Autonomy

This paper presents a mini-review of the current state of research in mobile manipulators with variable levels of autonomy, emphasizing their associated challenges and application environments. The need for mobile manipulators in different environments is evident due to the unique challenges and risks each presents. Many systems deployed in these environments are not fully autonomous, requiring human-robot teaming to ensure safe and reliable operations under uncertainties. Through this analysis, we identify gaps and challenges in the literature on Variable Autonomy, including cognitive workload and communication delays, and propose future directions, including whole-body Variable Autonomy for mobile manipulators, virtual reality frameworks, and large language models to reduce operators' complexity and cognitive load in some challenging and uncertain scenarios.

Semi-autonomous Robotic Disassembly Enhanced by Mixed Reality

In this study, we introduce "SARDiM," a modular semi-autonomous platform enhanced with mixed reality for industrial disassembly tasks. Through a case study focused on EV battery disassembly, SARDiM integrates Mixed Reality, object segmentation, teleoperation, force feedback, and variable autonomy. Utilising the ROS, Unity, and MATLAB platforms, alongside a joint impedance controller, SARDiM facilitates teleoperated disassembly. The approach combines FastSAM for real-time object segmentation, generating data which is subsequently processed through a cluster analysis algorithm to determine the centroid and orientation of the components, categorizing them by size and disassembly priority. This data guides the MoveIt platform in trajectory planning for the Franka Robot arm. SARDiM provides the capability to switch between two teleoperation modes: manual and semi-autonomous with variable autonomy. Each was evaluated using four different Interface Methods (IM): direct view, monitor feed, mixed reality with monitor feed, and point cloud mixed reality. Evaluations across the eight IMs demonstrated a 40.61% decrease in joint limit violations using Mode 2. Moreover, Mode 2-IM4 outperformed Mode 1-IM1 by achieving a 2.33%-time reduction while considerably increasing safety, making it optimal for operating in hazardous environments at a safe distance, with the same ease of use as teleoperation with a direct view of the environment.

Learning effects in variable autonomy human-robot systems: how much training is enough?

This paper investigates learning effects and human operator training practices in variable autonomy robotic systems. These factors are known to affect performance of a human-robot system and are frequently overlooked. We present the results from an experiment inspired by a search and rescue scenario in which operators remotely controlled a mobile robot with either Human-Initiative (HI) or Mixed-Initiative (MI) control. Evidence suggests learning in terms of primary navigation task and secondary (distractor) task performance. Further evidence is provided that MI and HI performance in a pure navigation task is equal. Lastly, guidelines are proposed for experimental design and operator training practices.

Imitation learning for sim-to-real transfer of robotic cutting policies based on residual Gaussian process disturbance force model

Robotic cutting, or milling, plays a significant role in applications such as disassembly, decommissioning, and demolition. Planning and control of cutting in real-world scenarios in uncertain environments is a complex task, with the potential to benefit from simulated training environments. This letter focuses on sim-to-real transfer for robotic cutting policies, addressing the need for effective policy transfer from simulation to practical implementation. We extend our previous domain generalisation approach to learning cutting tasks based on a mechanistic model-based simulation framework, by proposing a hybrid approach for sim-to-real transfer based on a milling process force model and residual Gaussian process (GP) force model, learned from either single or multiple real-world cutting force examples. We demonstrate successful sim-to-real transfer of a robotic cutting policy without the need for fine-tuning on the real robot setup. The proposed approach autonomously adapts to materials with differing structural and mechanical properties. Furthermore, we demonstrate the proposed method outperforms fine-tuning or re-training alone.

Haptic-guided assisted telemanipulation approach for grasping desired objects from heaps

This paper presents an assisted telemanipulation framework for reaching and grasping desired objects from clutter. Specifically, the developed system allows an operator to select an object from a cluttered heap and effortlessly grasp it, with the system assisting in selecting the best grasp and guiding the operator to reach it. To this end, we propose an object pose estimation scheme, a dynamic grasp re-ranking strategy, and a reach-to-grasp hybrid force/position trajectory guidance controller. We integrate them, along with our previous SpectGRASP grasp planner, into a classical bilateral teleoperation system that allows to control the robot using a haptic device while providing force feedback to the operator. For a user-selected object, our system first identifies the object in the heap and estimates its full six degrees of freedom (DoF) pose. Then, SpectGRASP generates a set of ordered, collision-free grasps for this object. Based on the current location of the robot gripper, the proposed grasp re-ranking strategy dynamically updates the best grasp. In assisted mode, the hybrid controller generates a zero force-torque path along the reach-to-grasp trajectory while automatically controlling the orientation of the robot. We conducted real-world experiments using a haptic device and a 7-DoF cobot with a 2-finger gripper to validate individual components of our telemanipulation system and its overall functionality. Obtained results demonstrate the effectiveness of our system in assisting humans to clear cluttered scenes.

Local Region-to-Region Mapping-based Approach to Classify Articulated Objects

Autonomous robots operating in real-world environments encounter a variety of objects that can be both rigid and articulated in nature. Having knowledge of these specific object properties not only helps in designing appropriate manipulation strategies but also aids in developing reliable tracking and pose estimation techniques for many robotic and vision applications. In this context, this paper presents a registration-based local region-to-region mapping approach to classify an object as either articulated or rigid. Using the point clouds of the intended object, the proposed method performs classification by estimating unique local transformations between point clouds over the observed sequence of movements of the object. The significant advantage of the proposed method is that it is a constraint-free approach that can classify any articulated object and is not limited to a specific type of articulation. Additionally, it is a model-free approach with no learning components, which means it can classify whether an object is articulated without requiring any object models or labelled data. We analyze the performance of the proposed method on two publicly available benchmark datasets with a combination of articulated and rigid objects. It is observed that the proposed method can classify articulated and rigid objects with good accuracy.

A Supervised Machine Learning Approach to Operator Intent Recognition for Teleoperated Mobile Robot Navigation

In applications that involve human-robot interaction (HRI), human-robot teaming (HRT), and cooperative human-machine systems, the inference of the human partner's intent is of critical importance. This paper presents a method for the inference of the human operator's navigational intent, in the context of mobile robots that provide full or partial (e.g., shared control) teleoperation. We propose the Machine Learning Operator Intent Inference (MLOII) method, which a) processes spatial data collected by the robot's sensors; b) utilizes a supervised machine learning algorithm to estimate the operator's most probable navigational goal online. The proposed method's ability to reliably and efficiently infer the intent of the human operator is experimentally evaluated in realistically simulated exploration and remote inspection scenarios. The results in terms of accuracy and uncertainty indicate that the proposed method is comparable to another state-of-the-art method found in the literature.

Towards Reuse and Recycling of Lithium-ion Batteries: Tele-robotics for Disassembly of Electric Vehicle Batteries

Disassembly of electric vehicle batteries is a critical stage in recovery, recycling and re-use of high-value battery materials, but is complicated by limited standardisation, design complexity, compounded by uncertainty and safety issues from varying end-of-life condition. Telerobotics presents an avenue for semi-autonomous robotic disassembly that addresses these challenges. However, it is suggested that quality and realism of the user's haptic interactions with the environment is important for precise, contact-rich and safety-critical tasks. To investigate this proposition, we demonstrate the disassembly of a Nissan Leaf 2011 module stack as a basis for a comparative study between a traditional asymmetric haptic-'cobot' master-slave framework and identical master and slave cobots based on task completion time and success rate metrics. We demonstrate across a range of disassembly tasks a time reduction of 22%-57% is achieved using identical cobots, yet this improvement arises chiefly from an expanded workspace and 1:1 positional mapping, and suffers a 10-30% reduction in first attempt success rate. For unbolting and grasping, the realism of force feedback was comparatively less important than directional information encoded in the interaction, however, 1:1 force mapping strengthened environmental tactile cues for vacuum pick-and-place and contact cutting tasks.