Force, impedance and trajectory learning for contact tooling and haptic identification

Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling

Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach

Robots and tools for remodeling bone

The field of robotic surgery has progressed from small teams of researchers repurposing industrial robots, to a competitive and highly innovative subsection of the medical device industry. Surgical robots allow surgeons to perform tasks with greater ease, accuracy, or safety, and fall under one of four levels of autonomy; active, semi-active, passive, and remote manipulator. The increased accuracy afforded by surgical robots has allowed for cementless hip arthroplasty, improved postoperative alignment following knee arthroplasty, and reduced duration of intraoperative fluoroscopy among other benefits. Cutting of bone has historically used tools such as hand saws and drills, with other elaborate cutting tools now used routinely to remodel bone. Improvements in cutting accuracy and additional options for safety and monitoring during surgery give robotic surgeries some advantages over conventional techniques. This article aims to provide an overview of current robots and tools with a common target tissue of bone, proposes a new process for defining the level of autonomy for a surgical robot, and examines future directions in robotic surgery

Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception.

When moving a piano or dancing tango with a partner, how should I control my arm muscles to sense their movements and follow or guide them smoothly? Here we observe how physically connected pairs tracking a moving target with the arm modify muscle coactivation with their visual acuity and the partner's performance. They coactivate muscles to stiffen the arm when the partner's performance is worse and relax with blurry visual feedback. Computational modeling shows that this adaptive sensing property cannot be explained by the minimization of movement error hypothesis that has previously explained adaptation in dynamic environments. Instead, individuals skillfully control the stiffness to guide the arm toward the planned motion while minimizing effort and extracting useful information from the partner's movement. The central nervous system regulates muscle activation to guide motion with accurate task information from vision and haptics while minimizing the metabolic cost. As a consequence, the partner with the most accurate target information leads the movement.NEW & NOTEWORTHY Our results reveal that interacting humans inconspicuously modulate muscle activation to extract accurate information about the common target while considering their own and the partner's sensorimotor noise. A novel computational model was developed to decipher the underlying mechanism: muscle coactivation is adapted to combine haptic information from the interaction with the partner and own visual information in a stochastically optimal manner. This improves the prediction of the target position with minimal metabolic cost in each partner, resulting in the lead of the partner with the most accurate visual information

Fractal Impedance for Passive Controllers

There is increasing interest in control frameworks capable of moving robots from industrial cages to unstructured environments and coexisting with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need for a general control framework that improves interaction robustness and motion dynamics. Passive controllers show promising results in this direction; however, they often rely on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a fractal attractor is proposed to implement a variable impedance controller that can retain passivity without relying on energy tanks. The controller generates a fractal attractor around the desired state using an asymptotic stable potential field, making the controller robust to discretization and numerical integration errors. The results prove that it can accurately track both trajectories and end-effector forces during interaction. Therefore, these properties make the controller ideal for applications requiring robust dynamic interaction at the end-effector.Comment: Video Available at https://youtu.be/S06_hqn3Nv

Learning to push and learning to move: The adaptive control of contact forces

To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in \u201ccompatible pairs\u201d connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions

Human adaptive haptic sensing

How do humans physically interact with the environment or with other humans? It is well known that the nervous system can modify the body’s stiffness by selectively cocontracting muscles to shape the mechanical interaction with the environment, but how this influences haptic perception is not known. This thesis examines whether humans can adapt muscles’ activation to influence their perception of the physical interaction with the environment. This question is investigated by conducting behavioural experiments using dedicated robotic interfaces to study sensorimotor interactions in the presence of haptic and visual perturbations. Hypotheses about the underlying mechanism are then tested through mathematical modelling and simulations. Chapter 1 reviews related frameworks and introduces the most relevant questions addressed in this work. Chapter 2 then shows that the central nervous system (CNS) can voluntarily adapt muscle cocontraction to increase haptic sensitivity. In an experiment, participants tracked a randomly moving target with visual noise while being physically guided by a virtual elastic band, where the band’s stiffness was controlled by their muscle coactivation. The results show that participants learned to increase cocontraction with visual noise and decrease it when the guidance is incongruent with the visual target. The adaptation law governing the regulation of the body’s stiffness by the CNS is then derived through computational modelling. This model is designed to maximise visuo-haptic information while minimising metabolic cost, thus trading off sensory information with energy. Further, it is shown in Chapter 3 that when the subjects are coupled via a tuneable connection to a robotic guidance designed to hinder their tracking through perturbations at the turning points (where participants physiologically increase cocontraction), they adapted cocontraction to reduce the impact of perturbations on performance. These results highlight the CNS ability to modify the muscle activation patterns to improve performance with minimal effort. Chapter 4 tests the robustness of human adaptive haptic sensing introduced in the previous chapters for human-human physical interaction. For example, in tango dancing physical contact provides haptic information of the partner’s action required to coordinate the movements. During such physical interactions, should one keep the arms compliant so that the partner can correct the motion, or should one stiffen them to better keep along the planned movement? Using a tracking task in which a dyad is coupled via a rigid connection, subjects readily adapted the compliance of their limb depending on both the accuracy of the partner’s and their own movement. The same computational model introduced in Chapter 2 could explain these results and predict the experimentally observed cocontraction adaptation. This suggests that the minimisation of prediction error and energy is a general principle also holding in interpersonal interactions. Altogether, these findings shed light on how humans can adapt haptic sensing by changing body properties, and propose a novel framework to interpret visuo-haptic perception for interaction with the environment and other humans.Open Acces

Master of Science

thesisIncreased demand for powered wheelchairs and their inherent mobility limitations have prompted the development of omnidirectional wheelchairs. These wheelchairs provide improved mobility in confined spaces, but can be more difficult to control and impact the ability of the user to embody the wheelchair. We hypothesize that control and embodiment of omnidirectional wheelchairs can be improved by providing intuitive control with three degree of freedom (3-DOF) haptic feedback that directly corresponds to the degrees of freedom of an omnidirectional wheelchair. This thesis introduces a novel 3-DOF Haptic Joystick designed for the purpose of controlling omnidirectional wheelchairs. When coupled with range finders, it is able to provide the user with feedback that improves the operator's awareness of the area surrounding the vehicle and assists the driver in obstacle avoidance. The haptic controller design and a stability analysis of the coupled wheelchair joystick systems are presented. Experimental results from the coupled systems validate the ability of the controller to influence the trajectory of the wheelchair and assist in obstacle avoidance

Design and development of the ‘POD Adventures’ smartphone game: a blended problem-solving intervention for adolescent mental health in India

Introduction: Digital technology platforms offer unparalleled opportunities to reach vulnerable adolescents at scale and overcome many barriers that exist around conventional service provision. This paper describes the design and development of POD Adventures, a blended problem-solving game-based intervention for adolescents with or at risk of anxiety, depression and conduct difficulties in India. This intervention was developed as part of the PRemIum for ADolEscents (PRIDE) research programme, which aims to establish a suite of transdiagnostic psychological interventions organised around a stepped care system in Indian secondary schools. Methods and materials: Intervention development followed a person-centered approach consisting of four iterative activities: (i) review of recent context-specific evidence on mental health needs and preferences for the target population of school-going Indian adolescents, including a multiple stakeholder analysis of school counselling priorities and pilot studies of a brief problem-solving intervention; (ii) new focus group discussions with N=46 student participants and N=8 service providers; (iii) co-design workshops with N=22 student participants and N=8 service providers; and (iv) user-testing with N=50 student participants. Participants were aged 12-17 years and recruited from local schools in New Delhi and Goa, including a subgroup with self-identified mental health needs (N=6). Results: Formative data from existing primary sources, new focus groups and co-design workshops supported a blended format for delivering a brief problem-solving intervention, with counsellors supporting use of a game-based app on ‘offline’ smartphones. User-testing with prototypes identified a need for simplification of language, use of concrete examples of concepts and practice elements to enhance engagement. There were also indications that participants most valued relatability and interactivity within real-world stories with judicious support from an in-app guide. The final prototype comprised a set of interactive and gamified vignettes and a structured set of problem-solving questions to consolidate and generalise learning while encouraging real-world application. Discussion: Findings shaped the design of POD Adventures and its delivery as an open-access blended intervention for secondary school students with a felt need for psychological support, consistent with an early intervention paradigm. A randomised controlled trial is planned to evaluate processes and impacts of POD Adventures when delivered for help-seeking students in low-resource school settings

Impedance Learning for Human-Guided Robots in Contact With Unknown Environments

Previous works have developed impedance control to increase safety and improve performance in contact tasks, where the robot is in physical interaction with either an environment or a human user. This article investigates impedance learning for a robot guided by a human user while interacting with an unknown environment. We develop automatic adaptation of robot impedance parameters to reduce the effort required to guide the robot through the environment, while guaranteeing interaction stability. For nonrepetitive tasks, this novel adaptive controller can attenuate disturbances by learning appropriate robot impedance. Implemented as an iterative learning controller, it can compensate for position dependent disturbances in repeated movements. Experiments demonstrate that the robot controller can, in both repetitive and nonrepetitive tasks: first, identify and compensate for the interaction, second, ensure both contact stability (with reduced tracking error) and maneuverability (with less driving effort of the human user) in contact with real environments, and third, is superior to previous velocity-based impedance adaptation control methods