Constrained motion planning and execution for soft robots

There are many reasons why a compliant robot is expected to perform better than a rigid one in interaction tasks, which include limitation of interaction forces, resilience to modeling errors, robustness, naturalness of motion, and energy efficiency. Most of these reasons are apparent if one thinks of how the human body interacts with its environment. However, most of the work in robotic planning and control of interaction has been traditionally developed for rigid robot models. Indeed, planning and control for compliant robots can be substantially harder. In this thesis, I propose the point of view that the difficulties encountered in planning and control for soft robots are at least in part due to the fact that the same approaches previously used for rigid robots are used as a starting point and adapted. On the opposite, if new methods are considered that start from consideration of compliance from the very beginning, the planning and control problems can be of comparable difficulty, or even substantially simpler, than their rigid counterpart. I will argue this thesis with two main examples. The first part of this thesis presents a new approach to integrate motion planning and control for robots in interaction. One of the peculiarities of interaction tasks is that the robot limbs and the environment form "closed kinematic chains". If rigid models are considered, the dynamics of robots in interaction become constrained, and Differential Algebraic Equations replace Ordinary Differential Equations, i.e. typically a much harder problem to deal with. However, in the thesis I show that this is not necessarily so. Indeed, consideration of compliance allows to have a more tractable mathematical model of interacting systems, and to introduce more sophisticated control approaches. Specifically, we present a novel geometric control scheme under which for constrained robot systems we achieve decoupled interaction control (i.e. make position errors irrelevant to force control, and viceversa). Based on this result, it is possible to decouple the planning problem in two separate aspects. On one side, we make dealing with motion planning of the constrained system easier by relaxing the geometric constraint, i.e. replacing the lower--dimensional constraint manifold with a narrow but full-dimensional boundary layer. This allows us to plan motion using state-of-the-art methods, such as RRT*, on points within the boundary layer, which we can efficiently sample. On the other side we control interaction forces, i.e. forces generated by displacements in the perpendicular direction to the tangent space of the constraint manifold. Thanks to the (locally) noninteracting control characteristic of our scheme, the two controllers can be applied separately and in sequence, so that the interaction force controller can correct for any discrepancies resulting from the boundary layer approximation used in the constrained position controller. The geometric noninteracting controller can be applied both in simulation for planning, and in real time for execution control. Moreover, while it does rely on considering a model of compliance in the system, it does not make any assumption on the amount of compliance in the system - or in other words, it applies equally well to stiff but elastic robots. The final outcome of the two-stage planner is an effective (possibly optimal from RRT*) trajectory that satisfies constraint with arbitrarily good approximation, asymptotically rejecting perturbations coming from sampled displacements. The second part of this thesis is dedicated to study grasp planning for hands that are simple -- in the sense of low number of actuated degrees of freedom -- but soft, i.e. continuously deformable in an infinity of possible shapes through interaction with objects. Once again, the use of such "soft hands" brings about a change of paradigm in grasp planning with respect to classical rigid multi-dof grasp planning, which only apparently makes the problem harder. However, in this thesis I show that thanks to the correct combination of compliance and underactuation of soft hands, together with the set of all possible physical interactions between the hand, the object and the environment, the grasping problem can be redefined. The new definition includes the possible combination of hand-object functional interactions which I address as "Enabling Constraints". The use of Enabling Constraints constitutes a rather new challenge for existing grasping algorithms: adaptation to totally or partially unknown scenes remains a difficult task, toward which only some approaches have been investigated so far. In this thesis I present a first approach to the study of this novel kind of manipulation. It is based on an accurate simulation tool and starts from the considerations that hand compliance can be used to adapt to the shape of the surrounding objects and that rather than considering the environment as and obstacle to avoid, it can be used in turn to functionally shape the hand. I show that thanks to this functionality the problem of generating grasping postures for soft hands can be reduced to grasp basic geometries (e.g. cylinders or boxes) in which the geometry of the object can be decomposed

Robotic manipulation with flexible link fingers

A robot manipulator is a spatial mechanism consisting essentially of a series of bodies, called "links", connected to each other at "joints". The joints can be of various types: revolute, rotary, planar, prismatic, telescopic or combinations of these. A serial connection of the links results in an open-chain manipulator. Closed-chain manipulators result from non-serial (or parallel) connections between links. Actuators at the joints of the manipulator provide power for motion. A robot is usually not designed for a very specific or repetitive task which can be done equally well by task-specific machines. Its strength lies in its ability to handle a range of tasks by virtue of being "re-programmable". Therefore, in addition to the mechanical hardware two other elements are integral to the description of a robot: sensors and control. With the advent of micro-electronics and digital computers the availability of sensors is ever increasing and the control is usually done by software executed by computers which also collect the sensory data. It is possible to model quite accurately, the dynamics of robot manipulators for purposes of control. However, for most practical robots the models are complex and numerically intensive to calculate in real-time. Traditional analyses of robot manipulators consider the whole mechanism to be rigid. Relaxation of the assumption of rigidity leads to further complication of the dynamics of the manipulator, leading to more difficulties in control. The overall motion of the manipulator is augmented by additional motion due to the dynamics of flexibility which must be considered. Sensing is also made more difficult. However, the ability to control robots with significant structural flexibilities, referred to as flexible robots in the rest of this thesis, influences robotics in many ways. It allows for consideration of new applications, observance of less conservative structural design and performance enhancements in certain classes of robotic tasks, which will be addressed in greater detail in the sections which follow

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

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

Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators

In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified

Cognitive Task Planning for Smart Industrial Robots

This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm. The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents. Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty. The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties. Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions. The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them

On the role of stability in animal morphology and neural control

Mechanical stability is vital for the fitness and survival of animals and is a crucial aspect of robot design and control. Stability depends on multiple factors, including the body\u27s intrinsic mechanical response and feedback control. But feedback control is more fragile than the body\u27s innate mechanical response or open-loop control strategies because of sensory noise and time-delays in feedback. This thesis examines the overarching hypothesis that stability demands have played a crucial role in how animal form and function arise through natural selection and motor learning. In two examples, finger contact and overall body stability, we investigated the relationship between morphology, open-loop control, and stability. By studying the stability of the internal degrees of freedom of a finger when pushing on a hard surface, we find that stability limits the force that we can produce and is a dominant aspect of the neural control of the finger\u27s muscles. In our study on whole body lateral stability during locomotion in terrestrial animals, we find that the overall body aspect ratio has evolved to ensure passive lateral stability on the uneven terrain of natural environments. Precisely gripping an object with the fingertips is a hallmark of human hand dexterity. In Chapter 2, we show how human fingers are intrinsically prone to a buckling-type postural instability and how humans use careful neural orchestration of our muscles so that the elastic response of our muscles can suppress the intrinsic instability. In Chapter 3, we extend these findings further to examine the nature of neuromuscular variability and how the nervous system deals with the need for muscle-induced stability. We find that there is structure to neuromuscular variability so that most of the variability lies within the subspace that does not affect stability. Inspired by the open-loop stable control of our index fingers, in Chapter 4, we derive open-loop stability conditions for a general mechanical linkage with arbitrary joint torques subjected to holonomic constraints. The solution that we derive is physically realizable as cable-driven active mechanical linkages. With a user-prescribed cable layout, we pose the problem of actuating the system to maintain stability while subject to goals like energy minimization as a convex optimization problem. We are thus able to use efficient optimization methods available for convex problems and demonstrate numerical solutions in examples inspired by the finger. Chapter 5 presents a general formulation of the stability criteria for active mechanical linkages subject to Pfaffian holonomic and non-holonomic constraints. Active mechanical linkages subject to multiple constraints represent the mechanics of systems spanning many domains and length scales, such as limbs and digits in animals and robots, and elastic networks like actin meshes in microscopic systems. We show that a constrained mechanical linkage with regular stiffness and damping, and circulation-free feedback, can only destabilize by static buckling when subject to holonomic constraints. In contrast, the same mechanical linkage, subject to a non-holonomic constraint, such as a skate contact, can exhibit either static buckling or flutter instability. Chapter 6 moves away from neural control and studies the shape of animal bodies and their relationship to stability in locomotion. We investigate why small land animals tend to have a crouched or sprawled posture, whereas larger animals are generally more upright. We propose a new hypothesis that the scaling of body aspect ratio with size is driven by the scale-dependent unevenness of natural terrain. We show that the scaling law arising from the need for stability on rough natural terrain correctly predicts the frontal aspect ratio scaling law across 335 terrestrial vertebrates and invertebrates, spanning eight orders of magnitude in mass so that smaller animals have a wider aspect ratio. We also carry out statistical analyses that consider the phylogenetic relationship among the species in our dataset to show that the scaling is not due to gradual changes of the traits over time. Thus, stability demands on natural terrain may have driven the macroevolution of body aspect ratio across terrestrial animals. Interrogating unstable and marginally stable behaviors has helped us identify the morphological and control features that allow animals to perform robustly in noisy environments where perfect sensory feedback cannot be assumed. Although the thesis identifies the `what\u27 and `why,\u27 further studies are needed to understand `how\u27 mechanics and development intertwine to give rise to control and form in growing and adapting biological organisms