OBJECTIVES: The digitalization of health records stands to improve decision-making at clinical, administrative, and policy level. Efforts follow various paths and are closely intertwined with health system and organizational configurations. Problems persist in both uptake and use. This study explores the digitalization trajectories of academic health centers (AHCs) to understand tensions between organizational and government strategies and their impact on digital development. MATERIALS AND METHODS: AHCs play a leadership role within health systems in data-driven improvement. This retrospective case study draws on documentary, observational, and interview data to compare digitalization efforts over 3 decades in 4 AHCs in the province of Quebec (Canada). RESULTS: At system level, strategy shifted from supporting multilayered development that encouraged bottom-up initiatives in the first decade of the 2000s, to harmonizing clinical information systems in a highly prescriptive manner after 2010. AHCs experienced the shift differently according to concurrent impacts of health system restructuring, and internal choices around electronic health record (EHR) systems and implementation priorities. Digital maturity remained low in all 4 AHCs. DISCUSSION: Coordination between system strategies and organizational strategies in AHCs was neglected in early digital development in Québec and improved only after an intense period of prescription and resistance. Confrontation highlighted tensions around different objectives at AHC and system level, competing missions within AHCs, and trade-offs between relying on commercial EHRs and developing publicly owned systems, all of which ultimately influence EHR implementation. CONCLUSION: The different experiences of focal organizations with digitalization underline the importance of adapting national strategies and providing support to implementers, building on acquired strengths, and arriving at the right balance of guidance from the top and autonomy to develop innovative capacities.

The use of the cognitive capabilties of humans to help guide the autonomy of robotics platforms in what is typically called “supervised-autonomy” is becoming more commonplace in robotics research. The work discussed in this paper presents an approach to a human-in-the-loop mode of robot operation that integrates high level human cognition and commanding with the intelligence and processing power of autonomous systems. Our framework for a “Supervised Remote Robot with Guided Autonomy and Teleoperation” (SURROGATE) is demonstrated on a robotic platform consisting of a pan-tilt perception head, two 7-DOF arms connected by a single 7-DOF torso, mounted on a tracked-wheel base. We present an architecture that allows high-level supervisory commands and intents to be specified by a user that are then interpreted by the robotic system to perform whole body manipulation tasks autonomously. We use a concept of “behaviors” to chain together sequences of “actions” for the robot to perform which is then executed real time.

This paper addresses the open challenge of planning quasi-static walking motions for robots with kinematically redundant limbs. Focusing on RoboSimian, a quadrupedal robot developed by the Jet Propulsion Laboratory (JPL), we develop a practical method for generating statically stable walking motions by pre-computing a reduced dimensional inverse kinematics (IK) lookup table with certain uniqueness and smoothness properties. We then use that lookup table to generate IK solutions at the beginning and end of walking phases (e.g., swing, body shift, etc), and connect these waypoints using the Rapidly exploring Random Tree Connect (RRT-Connect) algorithm [1]. Thus, we avoid arbitrarily choosing an IK solution at the goal (that may turn out to be difficult to reach from the start) by setting this choice through design and use of a task-specific lookup table, which can be analyzed offline. Our approach also introduces a complementary formulation of the RRT-Connect configuration space that addresses contact and closure constraints by using the forward kinematics of one stance leg to determine the body pose while treating additional stance limbs as dependent on the body pose and solving their inverse kinematics with IK table lookup. We demonstrate an implementation of some of this framework on RoboSimian and discuss generalizations and extensions.

This paper introduces a tactile or contact method whereby an autonomous robot equipped with suitable sensors can choose the next sensing action involving touch in order to accurately localize an object in its environment. The method uses an information gain metric based on the uncertainty of the object's pose to determine the next best touching action. Intuitively, the optimal action is the one that is the most informative. The action is then carried out and the state of the object's pose is updated using an estimator. The method is further extended to choose the most informative action to simultaneously localize and estimate the object's model parameter or model class. Results are presented both in simulation and in experiment on the DARPA Autonomous Robotic Manipulation Software (ARM-S) robot.

This paper develops an estimation framework for sensor-guided dual-arm manipulation of a rigid object. Using an unscented Kalman Filter (UKF), the approach combines both visual and kinesthetic information to track both the manipulators and object. From visual updates of the object and manipulators, and tactile updates, the method estimates both the robot's internal state and the object's pose. Nonlinear constraints are incorporated into the framework to deal with the an additional arm and ensure the state is consistent. Two frameworks are compared in which the first framework run two single arm filters in parallel and the second consists of the augment dual arm filter with nonlinear constraints. Experiments on a wheel changing task are demonstrated using the DARPA ARM-S system, consisting of dual Barrett- WAM manipulators.

This paper develops an estimation framework for sensor-guided manipulation of a rigid object via a robot arm. Using an unscented Kalman Filter (UKF), the method combines dense range information (from stereo cameras and 3D ranging sensors) as well as visual appearance features and silhouettes of the object and manipulator to track both an object-fixed frame location as well as a manipulator tool or palm frame location. If available, tactile data is also incorporated. By using these different imaging sensors and different imaging properties, we can leverage the advantages of each sensor and each feature type to realize more accurate and robust object and reference frame tracking. The method is demonstrated using the DARPA ARM-S system, consisting of a Barrett™WAM manipulator.

This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.

This paper develops a method to fuse stereo vision, force-torque sensor, and joint angle encoder measurements to estimate and track the location of a grasped object within the hand. We pose the problem as a hybrid systems estimation problem, where the continuous states are the object 6D pose, finger contact location, wrist-to-camera transform and the discrete states are the finger contact modes with the object. This paper develops the key measurement equations that govern the fusion process. Experiments with a Barrett Hand, Bumblebee 2 stereo camera, and an ATI omega force-torque sensor validate and demonstrate the method.