Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning

Telerobotic systems must adapt to new environmental conditions and deal with high uncertainty caused by long-time delays. As one of the best alternatives to human-level intelligence, Reinforcement Learning (RL) may offer a solution to cope with these issues. This paper proposes to integrate RL with the Model Mediated Teleoperation (MMT) concept. The teleoperator interacts with a simulated virtual environment, which provides instant feedback. Whereas feedback from the real environment is delayed, feedback from the model is instantaneous, leading to high transparency. The MMT is realized in combination with an intelligent system with two layers. The first layer utilizes Dynamic Movement Primitives (DMP) which accounts for certain changes in the avatar environment. And, the second layer addresses the problems caused by uncertainty in the model using RL methods. Augmented reality was also provided to fuse the avatar device and virtual environment models for the teleoperator. Implemented on DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methods are able to find different solutions when changes are applied to the object position after the demonstration. The results also show DMPs to be effective at adapting to new conditions where there is no uncertainty involved

Towards Skill Transfer via Learning-Based Guidance in Human-Robot Interaction

This thesis presents learning-based guidance (LbG) approaches that aim to transfer skills from human to robot. The approaches capture the temporal and spatial information of human motions and teach robot to assist human in human-robot collaborative tasks. In such physical human-robot interaction (pHRI) environments, learning from demonstrations (LfD) enables this transferring skill. Demonstrations can be provided through kinesthetic teaching and/or teleoperation. In kinesthetic teaching, humans directly guide robot’s body to perform a task while in teleoperation, demonstrations can be done through motion/vision-based systems or haptic devices. In this work, the LbG approaches are developed through kinesthetic teaching and teleoperation in both virtual and physical environments. First, this thesis compares and analyzes the capability of two types of statistical models, generative and discriminative, to generate haptic guidance (HG) forces as well as segment and recognize gestures for pHRI that can be used in virtual minimally invasive surgery (MIS) training. In this learning-based approach, the knowledge and experience of experts are modeled to improve the unpredictable motions of novice trainees. Two statistical models, hidden Markov model (HMM) and hidden Conditional Random Fields (HCRF), are used to learn gestures from demonstrations in a virtual MIS related task. The models are developed to automatically recognize and segment gestures as well as generate guidance forces. In practice phase, the guidance forces are adaptively calculated in real time regarding gesture similarities among user motion and the gesture models. Both statistical models can successfully capture the gestures of the user and provide adaptive HG, however, results show the superiority of HCRF, as a discriminative method, compared to HMM, as a generative method, in terms of user performance. In addition, LbG approaches are developed for kinesthetic HRI simulations that aim to transfer the skills of expert surgeons to resident trainees. The discriminative nature of HCRF is incorporated into the approach to produce LbG forces and discriminate the skill levels of users. To experimentally evaluate this kinesthetic-based approach, a femur bone drilling simulation is developed in which residents are provided haptic feedback based on real computed tomography (CT) data that enable them to feel the variable stiffness of bone layers. Orthepaedic surgeons require to adjust drilling force since bone layers have different stiffness. In the learning phase, using the simulation, an expert HCRF model is trained from expert surgeons demonstration to learn the stiffness variations of different bone layers. A novice HCRF model is also developed from the demonstration of novice residents to discriminate the skill levels of a new trainee. During the practice phase, the learning-based approach, which encoded the stiffness variations, guides the trainees to perform training tasks similar to experts motions. Finally, in contrast to other parts of the thesis, an LbG approach is developed through teleoperation in physical environment. The approach assists operators to navigate a teleoperated robot through a haptic steering wheel and a haptic gas pedal. A set of expert operator demonstrations are used to develop maneuvering skill model. The temporal and spatial variation of demonstrations are learned using HMM as the skill model. A modified Gaussian Mixture regression (GMR) in combination with the HMM is also developed to robustly produce the motion during reproduction. The GMR calculates outcome motions from a joint probability density function of data rather than directly model the regression function. In addition, the distance between the robot and obstacles is incorporated into the impedance control to generate guidance forces that also assist operators with avoiding obstacle collisions. Using different forms of variable impedance control, guidance forces are computed in real time with respect to the similarities between the maneuver of users and the skill model. This encourages users to navigate a robot similar to the expert operators. The results show that user performance is improved in terms of number of collisions, task completion time, and average closeness to obstacles

Generative Models for Learning Robot Manipulation Skills from Humans

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to direct trajectory learning from demonstrations, many problems arise in interactive robotic applications that require higher contextual level understanding of the environment. This requires learning invariant mappings in the demonstrations that can generalize across different environmental situations such as size, position, orientation of objects, viewpoint of the observer, etc. In this thesis, we address this challenge by encapsulating invariant patterns in the demonstrations using probabilistic learning models for acquiring dexterous manipulation skills. We learn the joint probability density function of the demonstrations with a hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The model exploits the invariant segments (also termed as sub-goals, options or actions) in the demonstrations and adapts the movement in accordance with the external environmental situations such as size, position and orientation of the objects in the environment using a task-parameterized formulation. We incorporate high-dimensional sensory data for skill acquisition by parsimoniously representing the demonstrations using statistical subspace clustering methods and exploit the coordination patterns in latent space. To adapt the models on the fly and/or teach new manipulation skills online with the streaming data, we formulate a non-parametric scalable online sequence clustering algorithm with Bayesian non-parametric mixture models to avoid the model selection problem while ensuring tractability under small variance asymptotics. We exploit the developed generative models to perform manipulation skills with remotely operated vehicles over satellite communication in the presence of communication delays and limited bandwidth. A set of task-parameterized generative models are learned from the demonstrations of different manipulation skills provided by the teleoperator. The model captures the intention of teleoperator on one hand and provides assistance in performing remote manipulation tasks on the other hand under varying environmental situations. The assistance is formulated under time-independent shared control, where the model continuously corrects the remote arm movement based on the current state of the teleoperator; and/or time-dependent autonomous control, where the model synthesizes the movement of the remote arm for autonomous skill execution. Using the proposed methodology with the two-armed Baxter robot as a mock-up for semi-autonomous teleoperation, we are able to learn manipulation skills such as opening a valve, pick-and-place an object by obstacle avoidance, hot-stabbing (a specialized underwater task akin to peg-in-a-hole task), screw-driver target snapping, and tracking a carabiner in as few as 4 - 8 demonstrations. Our study shows that the proposed manipulation assistance formulations improve the performance of the teleoperator by reducing the task errors and the execution time, while catering for the environmental differences in performing remote manipulation tasks with limited bandwidth and communication delays

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

PURPOSE: Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. METHODS: The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. RESULTS: Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. CONCLUSION: ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments