Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unified framework using a novel progressive learning approach comprised of three phases: i) a coarse learning phase for concept representation, ii) a fine learning phase for action generation, and iii) an imaginary learning phase for domain adaptation. Overall, this approach leads to a one-shot domain-adaptive imitation learning framework. We use robotic pouring as an example task to evaluate its effectiveness. Our results show that the method has several advantages over contemporary end-to-end imitation learning approaches, including an improved success rate for task execution and more efficient training for deep imitation learning. In addition, the generalizability to new domains is improved, as demonstrated here with novel backgrounds, target containers, and granule combinations in the experiment. We believe that the proposed method is broadly applicable to various industrial or domestic applications that involve deep imitation learning for robotic manipulation, and where the target scenarios are diverse and human demonstration data is limited. For project video, please check our website:https://sites.google.com/view/imitation-learning-tase2022. Note to Practitioners—The motivation of this paper is to develop a progressive learning framework, which can be used for both service and industrial robots to learn from human demonstrations, and then transfer the learned skill to different scenarios with ease. We use the robotic pouring task as an example to demonstrate the effectiveness of our proposed method, since pouring is an essential skill for service robots to assist humans’ daily lives, and can benefit robot automation in wet-lab industries. The aim of this research is to enable robots to obtain visuomotor skills (such as the pouring skill), and accomplish the tasks with a high success rate using our proposed progressive learning method. We conducted experiments to show that the proposed method has good performance, high data efficiency and evident generalizability. This is significant for intelligent robots working in various practical applications.

This paper presents a control scheme for force sensitive, gentle grasping with a Pisa/IIT anthropomorphic SoftHand equipped with a miniaturised version of the TacTip optical tactile sensor on all five fingertips. The tactile sensors provide high-resolution information about a grasp and how the fingers interact with held objects. We first describe a series of hardware developments for performing asynchronous sensor data acquisition and processing, resulting in a fast control loop sufficient for real-time grasp control. We then develop a novel grasp controller that uses tactile feedback from all five fingertip sensors simultaneously to gently and stably grasp 43 objects of varying geometry and stiffness, which is then applied to a human-to-robot handover task. These developments open the door to more advanced manipulation with underactuated hands via fast reflexive control using high-resolution tactile sensing.

In robots, nonprehensile manipulation operations such as pushing are a useful way of moving large, heavy, or unwieldy objects, moving multiple objects at once, or reducing uncertainty in the location or pose of objects. In this study, we propose a reactive and adaptive method for robotic pushing that uses rich feedback from a high-resolution optical tactile sensor to control push movements instead of relying on analytical or data-driven models of push interactions. Specifically, we use goal-driven tactile exploration to actively search for stable pushing configurations that cause the object to maintain its pose relative to the pusher while incrementally moving the pusher and object toward the target. We evaluate our method by pushing objects across planar and curved surfaces. For planar surfaces, we show that the method is accurate and robust to variations in initial contact position/angle, object shape, and start position; for curved surfaces, the performance is degraded slightly. An immediate consequence of our work is that it shows that explicit models of push interactions might be sufficient but are not necessary for this type of task. It also raises the interesting question of which aspects of the system should be modeled to achieve the best performance and generalization across a wide range of scenarios. Finally, it highlights the importance of testing on nonplanar surfaces and in other more complex environments when developing new methods for robotic pushing.

In this work, we report on how a sense of touch can be used to control an underactuated anthropomorphic robot hand, based on an integration that respects the hand’s mechanical functionality. Our focus is on integrating the sensorimotor control of the Pisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the principle of adaptive synergies, with the BRL tactile fingertip (TacTip), a soft biomimetic optical tactile sensor. We consider: (i) closed-loop tactile control to establish a light contact on an unknown held object, based on the structural similarity of the tactile image; and (ii) controlling the estimated pose of a held object, using a convolutional neural network approach developed for other TacTip sensors. Accurate control was found for a range of hard and soft objects (to sub-millimetre accuracy and a few degrees). Overall, this gives a foundation to endow soft robotic hands with human-like touch, with implications for autonomous grasping, manipulation, human-robot interaction and prosthetics.

There are a wide range of features that tactile contact provides, each with different aspects of information that can be used for object grasping, manipulation, and perception. In this paper inference of some key tactile features, tip displacement, contact location, shear direction and magnitude, is demonstrated by introducing a novel method of transducing a third dimension to the sensor data via Voronoi tessellation. The inferred features are displayed throughout the work in a new visualisation mode derived from the Voronoi tessellation; these visualisations create easier interpretation of data from an optical tactile sensor that measures local shear from displacement of internal pins (the TacTip). The output values of tip displacement and shear magnitude are calibrated to appropriate mechanical units and validate the direction of shear inferred from the sensor. We show that these methods can infer the direction of shear to ~2.3° without the need for training a classifier or regressor. The approach demonstrated here will increase the versatility and generality of the sensors and thus allow sensor to be used in more unstructured and unknown environments, as well as improve the use of these tactile sensors in more complex systems such as robot hands.

The papers in this special issue of the Computer Journal recognize the contribution of Professor Brian Randell, of Newcastle University, UK to the development of Dependable Software Systems. This introduction outlines significant aspects of his work, and introduces the work of the contributors.

This paper describes the process of introducing software for managing vaccine cold chain equipment to the ministries of health in several countries. The software, Cold Chain Equipment Manager (CCEM), is used to maintain a country wide inventory of vaccine refrigerators and cold rooms, and provides analysis and forecasting capabilities. The paper examines the characteristics of software used at the national level and the role of multiple stakeholders in adoption and use of systems. Based on deployment of the system in countries in Africa and Latin America, and interviews with immunization program officers, we conclude that this type of software can meet country needs in planning a more efficient vaccine cold chain and increasing system transparency provided that it is situated is a broader system committed to strengthening immunization programs.

The conference on grand challenges, held in Newcastle on 30 and 31 March 2004, occurred at a particularly opportune time. The strand on the educational aspects was particularly relevant and the idea innovative in the sense that this was the first occasion on which a grand challenge event with a focus on educational issues in computing had taken place. This paper provides some of the background and includes a distillation of the educational challenges that emerged from that event.

A description is given of a complete robot control facility built at the CIT Jet Propulsion Laboratory as part of a NASA telerobotic program to develop a state-of-the-art robot control environment for laboratory-based spacelike experiments. This system has the following features: separation of the computing facilities into local and remote sites, autonomous motion generation in joint or Cartesian coordinates, dual-arm force reflecting teleoperation with voice interaction between the operator and the robots, shared control between the autonomously generated motions and operator controlled teleoperation, and dual-arm coordinated trajectory generation. The system has been used to carry out realistic experiments such as the exchange of an orbital replacement unit, bolt turning, and door opening, using a mixture of autonomous actions and teleoperation, with either a single arm or two cooperating arms.>

The Robot Control C Library (RCCL) is a system for developing robot control programs in a UNIX environment, and has proven to be useful in research applications. Work undertaken in expanding RCCL to handle multiple robots, and upgrading its implementation to a multiple-processor microVAX/UNIX system is reported. This includes (1) reworking the primitive set to allow for the specification of multiple robot actions; (2) modifying the trajectory generation mechanism so that several robots may be controlled and coordinated at once; and (3) redesigning the system interface on top of which RCCL is built to allow the creation of multiple real-time robot control tasks interfaced to UNIX.>