Nonprehensile Manipulation via Multisensory Learning from Demonstration

Dexterous manipulation problem concerns control of a robot hand to manipulate an object in a desired manner. While classical dexterous manipulation strategies are based on stable grasping (or force closure), many human-like manipulation tasks do not maintain grasp stability, and often utilize the intrinsic dynamics of the object rather than the closed form of kinematic relation between the object and the robotic fingers. Such manipulation strategies are referred as nonprehensile or dynamic dexterous manipulation in the literature. Nonprehensile manipulation typically involves fast and agile movements such as throwing and flipping. Due to the complexity of such motions (which may involve impulsive dynamics) and uncertainties associated with them, it has been challenging to realize nonprehensile manipulation tasks in a reliable way. In this paper, we propose a new control strategy to realize practical nonprehensile manipulation tasks using a robot hand. The main idea of our control strategy are two-folds. Firstly, we make explicit use of multiple modalities of sensory data for the design of control law. Specifically, force data is employed for feedforward control while the position data is used for feedback (i.e. reactive) control. Secondly, control signals (both feedback and feedforward) are obtained by the multisensory learning from demonstration (LfD) experiments which are designed and performed for specific nonprehensile manipulation tasks in concern. We utilize various LfD frameworks such as Gaussian mixture model and Gaussian mixture regression (GMM/GMR) and hidden Markov model and GMR (HMM/GMR) to reproduce generalized motion profiles from the human expert's demonstrations. The proposed control strategy has been verified by experimental results on dynamic spinning task using a sensory-rich two-finger robotic hand. The control performance (i.e. the speed and accuracy of the spinning task) has also been compared with that of the classical dexterous manipulation based on finger gating

Legged Robots for Object Manipulation: A Review

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical Engineerin

A Reactive Planning Framework for Dexterous Robotic Manipulation

This thesis investigates a reactive motion planning and controller framework that enables robots to manipulate objects dexterously. We develop a robotic platform that can quickly and reliably replan actions based on sensed information. Robotic manipulation is subject to noise due to uncertainty in frictional contact information, and reactivity is key for robustness. The planning framework has been designed with generality in mind and naturally extends to a variety of robotic tasks, manipulators and sensors. This design is validated experimentally on an ABB IRB 14000 dual-arm industrial collaborative robot. In this research, we are interested in dexterous robot manipulation, where the key technology is to move an object from an initial location to a desired configuration. The robot makes use of a high resolution tactile sensor to monitor the progress of the task and drive the reactive behavior of the robot to counter mistakes or unaccounted environment conditions. The motion planning framework is integrated with a task planner that dictates the high-level manipulation behavior of the robot, as well as a low-level controller, that adapts robot motions based on measured tactile signaOutgoin

On Robotic Work-Space Sensing and Control

Industrial robots are fast and accurate when working with known objects at precise locations in well-structured manufacturing environments, as done in the classical automation setting. In one sense, limited use of sensors leaves robots blind and numb, unaware of what is happening in their surroundings. Whereas equipping a system with sensors has the potential to add new functionality and increase the set of uncertainties a robot can handle, it is not as simple as that. Often it is difficult to interpret the measurements and use them to draw necessary conclusions about the state of the work space. For effective sensor-based control, it is necessary to both understand the sensor data and to know how to act on it, giving the robot perception-action capabilities. This thesis presents research on how sensors and estimation techniques can be used in robot control. The suggested methods are theoretically analyzed and evaluated with a large focus on experimental verification in real-time settings. One application class treated is the ability to react fast and accurately to events detected by vision, which is demonstrated by the realization of a ball-catching robot. A new approach is proposed for performing high-speed color-based image analysis that is robust to varying illumination conditions and motion blur. Furthermore, a method for object tracking is presented along with a novel way of Kalman-filter initialization that can handle initial-state estimates with infinite variance. A second application class treated is robotic assembly using force control. A study of two assembly scenarios is presented, investigating the possibility of using force-controlled assembly in industrial robotics. Two new approaches for robotic contact-force estimation without any force sensor are presented and validated in assembly operations. The treated topics represent some of the challenges in sensor-based robot control, and it is demonstrated how they can be used to extend the functionality of industrial robots

Dyadic collaborative manipulation formalism for optimizing human-robot teaming

Dyadic collaborative Manipulation (DcM) is a term we use to refer to a team of two individuals, the agent and the partner, jointly manipulating an object. The two individuals partner together to form a distributed system, augmenting their manipulation abilities. Effective collaboration between the two individuals during joint action depends on: (i) the breadth of the agent’s action repertoire, (ii) the level of model acquaintance between the two individuals, (iii) the ability to adapt online of one’s own actions to the actions of their partner, and (iv) the ability to estimate the partner’s intentions and goals. Key to the successful completion of co-manipulation tasks with changing goals is the agent’s ability to change grasp-holds, especially in large object co-manipulation scenarios. Hence, in this work we developed a Trajectory Optimization (TO) method to enhance the repertoire of actions of robotic agents, by enabling them to plan and execute hybrid motions, i.e. motions that include discrete contact transitions, continuous trajectories and force profiles. The effectiveness of the TO method is investigated numerically and in simulation, in a number of manipulation scenarios with both a single and a bimanual robot. In addition, it is worth noting that transitions from free motion to contact is a challenging problem in robotics, in part due to its hybrid nature. Additionally, disregarding the effects of impacts at the motion planning level often results in intractable impulsive contact forces. To address this challenge, we introduce an impact-aware multi-mode TO method that combines hybrid dynamics and hybrid control in a coherent fashion. A key concept in our approach is the incorporation of an explicit contact force transmission model into the TO method. This allows the simultaneous optimization of the contact forces, contact timings, continuous motion trajectories and compliance, while satisfying task constraints. To demonstrate the benefits of our method, we compared our method against standard compliance control and an impact-agnostic TO method in physical simulations. Also, we experimentally validated the proposed method with a robot manipulator on the task of halting a large-momentum object. Further, we propose a principled formalism to address the joint planning problem in DcM scenarios and we solve the joint problem holistically via model-based optimization by representing the human's behavior as task space forces. The task of finding the partner-aware contact points, forces and the respective timing of grasp-hold changes are carried out by a TO method using non-linear programming. Using simulations, the capability of the optimization method is investigated in terms of robot policy changes (trajectories, timings, grasp-holds) to potential changes of the collaborative partner policies. We also realized, in hardware, effective co-manipulation of a large object by the human and the robot, including eminent grasp changes as well as optimal dyadic interactions to realize the joint task. To address the online adaptation challenge of joint motion plans in dyads, we propose an efficient bilevel formulation which combines graph search methods with trajectory optimization, enabling robotic agents to adapt their policy on-the-fly in accordance to changes of the dyadic task. This method is the first to empower agents with the ability to plan online in hybrid spaces; optimizing over discrete contact locations, contact sequence patterns, continuous trajectories, and force profiles for co-manipulation tasks. This is particularly important in large object co-manipulation tasks that require on-the-fly plan adaptation. We demonstrate in simulation and with robot experiments the efficacy of the bilevel optimization by investigating the effect of robot policy changes in response to real-time alterations of the goal. This thesis provides insight into joint manipulation setups performed by human-robot teams. In particular, it studies computational models of joint action and exploits the uncharted hybrid action space, that is especially relevant in general manipulation and co-manipulation tasks. It contributes towards developing a framework for DcM, capable of planning motions in the contact-force space, realizing these motions while considering impacts and joint action relations, as well as adapting on-the-fly these motion plans with respect to changes of the co-manipulation goals

Compliant control of Uni/ Multi- robotic arms with dynamical systems

Accomplishment of many interactive tasks hinges on the compliance of humans. Humans demonstrate an impressive capability of complying their behavior and more particularly their motions with the environment in everyday life. In humans, compliance emerges from different facets. For example, many daily activities involve reaching for grabbing tasks, where compliance appears in a form of coordination. Humans comply their handsâ motions with each other and with that of the object not only to establish a stable contact and to control the impact force but also to overcome sensorimotor imprecisions. Even though compliance has been studied from different aspects in humans, it is primarily related to impedance control in robotics. In this thesis, we leverage the properties of autonomous dynamical systems (DS) for immediate re-planning and introduce active complaint motion generators for controlling robots in three different scenarios, where compliance does not necessarily mean impedance and hence it is not directly related to control in the force/velocity domain. In the first part of the thesis, we propose an active compliant strategy for catching objects in flight, which is less sensitive to the timely control of the interception. The soft catching strategy consists in having the robot following the object for a short period of time. This leaves more time for the fingers to close on the object at the interception and offers more robustness than a âhardâ catching method in which the hand waits for the object at the chosen interception point. We show theoretically that the resulting DS will intercept the object at the intercept point, at the right time with the desired velocity direction. Stability and convergence of the approach are assessed through Lyapunov stability theory. In the second part, we propose a unified compliant control architecture for coordinately reaching for grabbing a moving object by a multi-arm robotic system. Due to the complexity of the task and of the system, each arm complies not only with the objectâs motion but also with the motion of other arms, in both task and joint spaces. At the task-space level, we propose a unified dynamical system that endows the multi-arm system with both synchronous and asynchronous behaviors and with the capability of smoothly transitioning between the two modes. At the joint space level, the compliance between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under self-collision avoidance constraints; formulated as a quadratic programming problem (QP) and solved in real-time. In the last part, we propose a compliant dynamical system for stably transitioning from free motions to contacts. In this part, by modulating the robot's velocity in three regions, we show theoretically and empirically that the robot can (I) stably touch the contact surface (II) at a desired location, and (III) leave the surface or stop on the surface at a desired point

Becoming Human with Humanoid

Nowadays, our expectations of robots have been significantly increases. The robot, which was initially only doing simple jobs, is now expected to be smarter and more dynamic. People want a robot that resembles a human (humanoid) has and has emotional intelligence that can perform action-reaction interactions. This book consists of two sections. The first section focuses on emotional intelligence, while the second section discusses the control of robotics. The contents of the book reveal the outcomes of research conducted by scholars in robotics fields to accommodate needs of society and industry

Robot manipulator skill learning and generalising through teleoperation

Robot manipulators have been widely used for simple repetitive, and accurate tasks in industrial plants, such as pick and place, assembly and welding etc., but it is still hard to deploy in human-centred environments for dexterous manipulation tasks, such as medical examination and robot-assisted healthcare. These tasks are not only related to motion planning and control but also to the compliant interaction behaviour of robots, e.g. motion control, force regulation and impedance adaptation simultaneously under dynamic and unknown environments. Recently, with the development of collaborative robotics (cobots) and machine learning, robot skill learning and generalising have attained increasing attention from robotics, machine learning and neuroscience communities. Nevertheless, learning complex and compliant manipulation skills, such as manipulating deformable objects, scanning the human body and folding clothes, is still challenging for robots. On the other hand, teleoperation, also namely remote operation or telerobotics, has been an old research area since 1950, and there have been a number of applications such as space exploration, telemedicine, marine vehicles and emergency response etc. One of its advantages is to combine the precise control of robots with human intelligence to perform dexterous and safety-critical tasks from a distance. In addition, telepresence allows remote operators could feel the actual interaction between the robot and the environment, including the vision, sound and haptic feedback etc. Especially under the development of various augmented reality (AR), virtual reality (VR) and wearable devices, intuitive and immersive teleoperation have received increasing attention from robotics and computer science communities. Thus, various human-robot collaboration (HRC) interfaces based on the above technologies were developed to integrate robot control and telemanipulation by human operators for robot skills learning from human beings. In this context, robot skill learning could benefit teleoperation by automating repetitive and tedious tasks, and teleoperation demonstration and interaction by human teachers also allow the robot to learn progressively and interactively. Therefore, in this dissertation, we study human-robot skill transfer and generalising through intuitive teleoperation interfaces for contact-rich manipulation tasks, including medical examination, manipulating deformable objects, grasping soft objects and composite layup in manufacturing. The introduction, motivation and objectives of this thesis are introduced in Chapter 1. In Chapter 2, a literature review on manipulation skills acquisition through teleoperation is carried out, and the motivation and objectives of this thesis are discussed subsequently. Overall, the main contents of this thesis have three parts: Part 1 (Chapter 3) introduces the development and controller design of teleoperation systems with multimodal feedback, which is the foundation of this project for robot learning from human demonstration and interaction. In Part 2 (Chapters 4, 5, 6 and 7), we studied primitive skill library theory, behaviour tree-based modular method, and perception-enhanced method to improve the generalisation capability of learning from the human demonstrations. And several applications were employed to evaluate the effectiveness of these methods.In Part 3 (Chapter 8), we studied the deep multimodal neural networks to encode the manipulation skill, especially the multimodal perception information. This part conducted physical experiments on robot-assisted ultrasound scanning applications.Chapter 9 summarises the contributions and potential directions of this thesis. Keywords: Learning from demonstration; Teleoperation; Multimodal interface; Human-in-the-loop; Compliant control; Human-robot interaction; Robot-assisted sonography