A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Waypoint-Based Imitation Learning for Robotic Manipulation

While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC). Waypoints can help address this problem by reducing the horizon of the learning problem for BC, and thus, the errors compounded over time. However, waypoint labeling is underspecified, and requires additional human supervision. Can we generate waypoints automatically without any additional human supervision? Our key insight is that if a trajectory segment can be approximated by linear motion, the endpoints can be used as waypoints. We propose Automatic Waypoint Extraction (AWE) for imitation learning, a preprocessing module to decompose a demonstration into a minimal set of waypoints which when interpolated linearly can approximate the trajectory up to a specified error threshold. AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10. Videos and code are available at https://lucys0.github.io/awe/Comment: The first two authors contributed equall

Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery

State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control

Collaborative and Cooperative Robotics Applications using Visual Perception

The objective of this Thesis is to develop novel integrated strategies for collaborative and cooperative robotic applications. Commonly, industrial robots operate in structured environments and in work-cell separated from human operators. Nowadays, collaborative robots have the capacity of sharing the workspace and collaborate with humans or other robots to perform complex tasks. These robots often operate in an unstructured environment, whereby they need sensors and algorithms to get information about environment changes. Advanced vision and control techniques have been analyzed to evaluate their performance and their applicability to industrial tasks. Then, some selected techniques have been applied for the first time to an industrial context. A Peg-in-Hole task has been chosen as first case study, since it has been extensively studied but still remains challenging: it requires accuracy both in the determination of the hole poses and in the robot positioning. Two solutions have been developed and tested. Experimental results have been discussed to highlight the advantages and disadvantages of each technique. Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. It is a complex task and requires to address multiple subproblems, in order to be accomplished, including object localization and grasp pose detection. Also for this class of issues some vision techniques have been analyzed. One of these has been adapted to be used in industrial scenarios. Moreover, as a second case study, a robot-to-robot object handover task in a partially structured environment and in the absence of explicit communication between the robots has been developed and validated. Finally, the two case studies have been integrated in two real industrial setups to demonstrate the applicability of the strategies to solving industrial problems

Recent Advancements in Augmented Reality for Robotic Applications: A Survey

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement

Capture and generalisation of close interaction with objects

Robust manipulation capture and retargeting has been a longstanding goal in both the fields of animation and robotics. In this thesis I describe a new approach to capture both the geometry and motion of interactions with objects, dealing with the problems of occlusion by the use of magnetic systems, and performing the reconstruction of the geometry by an RGB-D sensor alongside visual markers. This ‘interaction capture’ allows the scene to be described in terms of the spatial relationships between the character and the object using novel topological representations such as the Electric Parameters, which parametrise the outer space of an object using properties of the surface of the object. I describe the properties of these representations for motion generalisation and discuss how they can be applied to the problems of human-like motion generation and programming by demonstration. These generalised interactions are shown to be valid by demonstration of retargeting grasping and manipulation to robots with dissimilar kinematics and morphology using only local, gradient-based planning

Exploring Robot Teleoperation in Virtual Reality

This thesis presents research on VR-based robot teleoperation with a focus on remote environment visualisation in virtual reality, the effects of remote environment reconstruction scale in virtual reality on the human-operator's ability to control the robot and human-operator's visual attention patterns when teleoperating a robot from virtual reality. A VR-based robot teleoperation framework was developed, it is compatible with various robotic systems and cameras, allowing for teleoperation and supervised control with any ROS-compatible robot and visualisation of the environment through any ROS-compatible RGB and RGBD cameras. The framework includes mapping, segmentation, tactile exploration, and non-physically demanding VR interface navigation and controls through any Unity-compatible VR headset and controllers or haptic devices. Point clouds are a common way to visualise remote environments in 3D, but they often have distortions and occlusions, making it difficult to accurately represent objects' textures. This can lead to poor decision-making during teleoperation if objects are inaccurately represented in the VR reconstruction. A study using an end-effector-mounted RGBD camera with OctoMap mapping of the remote environment was conducted to explore the remote environment with fewer point cloud distortions and occlusions while using a relatively small bandwidth. Additionally, a tactile exploration study proposed a novel method for visually presenting information about objects' materials in the VR interface, to improve the operator's decision-making and address the challenges of point cloud visualisation. Two studies have been conducted to understand the effect of virtual world dynamic scaling on teleoperation flow. The first study investigated the use of rate mode control with constant and variable mapping of the operator's joystick position to the speed (rate) of the robot's end-effector, depending on the virtual world scale. The results showed that variable mapping allowed participants to teleoperate the robot more effectively but at the cost of increased perceived workload. The second study compared how operators used a virtual world scale in supervised control, comparing the virtual world scale of participants at the beginning and end of a 3-day experiment. The results showed that as operators got better at the task they as a group used a different virtual world scale, and participants' prior video gaming experience also affected the virtual world scale chosen by operators. Similarly, the human-operator's visual attention study has investigated how their visual attention changes as they become better at teleoperating a robot using the framework. The results revealed the most important objects in the VR reconstructed remote environment as indicated by operators' visual attention patterns as well as their visual priorities shifts as they got better at teleoperating the robot. The study also demonstrated that operators’ prior video gaming experience affects their ability to teleoperate the robot and their visual attention behaviours

The computerization of archaeology: survey on AI techniques

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version