Dataset Information

Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps.

ABSTRACT: In unstructured environments in people's homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a plate of food level to avoid spills or properly orienting a finger to push a button. We introduce a sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. Our time-dependent cost metric, learned from a set of demonstrations, encodes features of a task's motion that are consistent across the demonstrations and, hence, are likely required to successfully execute the task. Our sampling-based motion planner uses the learned cost metric to compute plans that simultaneously avoid obstacles and satisfy task constraints. The motion planner is asymptotically optimal and minimizes the Mahalanobis distance between the planned trajectory and the distribution of demonstrations in a feature space parameterized by the locations of task-relevant objects. The motion planner also leverages the distribution of the demonstrations to significantly reduce plan computation time. We demonstrate the method's effectiveness and speed using a small humanoid robot performing tasks requiring both obstacle avoidance and satisfaction of learned task constraints. NOTE TO PRACTITIONERS:Motivated by the desire to enable robots to autonomously operate in cluttered home and workplace environments, this paper presents an approach for intuitively training a robot in a manner that enables it to repeat the task in novel scenarios and in the presence of unforeseen obstacles in the environment. Based on user-provided demonstrations of the task, our method learns features of the task that are consistent across the demonstrations and that we expect should be repeated by the robot when performing the task. We next present an efficient algorithm for planning robot motions to perform the task based on the learned features while avoiding obstacles. We demonstrate the effectiveness of our motion planner for scenarios requiring transferring a powder and pushing a button in environments with obstacles, and we plan to extend our results to more complex tasks in the future.

SUBMITTER: Bowen C

PROVIDER: S-EPMC4535732 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps.

Bowen Chris C Ye Gu G Alterovitz Ron R

IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society 20150101 1

In unstructured environments in people's homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a plate of food level to avoid spills or properly orienting a finger to push a button. We introduce a sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. Our time-dependent cost metric, learned from a set of demonstrations, encodes features of ...[more]

PMID: 26279642

Similar Datasets

Project description:BackgroundAllied health assistants (AHAs) are support staff who complete patient and non-patient related tasks under the delegation of an allied health professional. Delegating patient related tasks to AHAs can benefit patients and allied health professionals. However, it is unclear whether the AHA workforce is utilised optimally in the provision of patient care. The purpose of this study was to determine the proportion of time AHAs spend on patient related tasks during their working day and any differences across level of AHA experience, clinical setting, and profession delegating the task.MethodsA time motion study was conducted using a self-report, task predominance work sampling method. AHAs were recruited from four publicly-funded health organisations in Victoria, Australia. AHAs worked with dietitians, occupational therapists, physiotherapists, podiatrists, social workers, speech pathologists, psychologists, and exercise physiologists. The primary outcome was quantity of time spent by AHAs on individual task-categories. Tasks were grouped into two main categories: patient or non-patient related activities. Data were collected from July 2020 to May 2021 using an activity capture proforma specifically designed for this study. Logistic mixed-models were used to investigate the extent to which level of experience, setting, and delegating profession were associated with time spent on patient related tasks.ResultsData from 51 AHAs showed that AHAs spent more time on patient related tasks (293 min/day, 64%) than non-patient related tasks (167 min/day, 36%). Time spent in community settings had lower odds of being delegated to patient related tasks than time in the acute hospital setting (OR 0.44, 95%CI 0.28 to 0.69, P < 0.001). Time delegated by exercise physiologists and dietitians was more likely to involve patient related tasks than time delegated by physiotherapists (exercise physiology: OR 3.77, 95% 1.90 to 7.70, P < 0.001; dietetics: OR 2.60, 95%CI 1.40 to 1.90, P = 0.003). Time delegated by other professions (e.g. podiatry, psychology) had lower odds of involving patient related tasks than physiotherapy (OR 0.37, 95%CI 0.16 to 0.85, P = 0.02).ConclusionAHAs may be underutilised in community settings, and by podiatrists and psychologists. These areas may be targeted to understand appropriateness of task delegation to optimise AHAs' role in providing patient care.

Project description:BackgroundMedication-related problems are an important cause of emergency department (ED) visits, and medication errors are reported in up to 60% of ED patients. Procedures such as medication reconciliation and medication review can identify and prevent medication-related problems and medication errors. However, this work is often time-consuming. In EDs without pharmacists, medication reconciliation is the physician's responsibility, in addition to the primary assignments of examining and diagnosing the patient. The aim of this study was to identify how much time ED physicians spend on medication-related tasks when no pharmacists are present in the EDs.MethodsAn observational time-and-motion study of physicians in three EDs in Northern Norway was conducted using Work Observation Method by Activity Timing (WOMBAT) to collect and time-stamp data. Observations were conducted in predefined two-hour observation sessions with a 1:1 relationship between observer and participant, during Monday to Friday between 8 am and 8 pm, from November 2020 to October 2021.ResultsIn total, 386 h of observations were collected during 225 observation sessions. A total of 8.7% of the physicians' work time was spent on medication-related tasks, of which most time was spent on oral communication about medications with other physicians (3.0%) and medication-related documentation (3.2%). Physicians spent 2.2 min per hour on medication reconciliation tasks, which includes retrieving medication-related information directly from the patient, reading/retrieving written medication-related information, and medication-related documentation. Physicians spent 85.6% of the observed time on non-medication-related clinical or administrative tasks, and the remaining time was spent standby or moving between tasks.ConclusionIn three Norwegian EDs, physicians spent 8.7% of their work time on medication-related tasks, and 85.6% on other clinical or administrative tasks. Physicians spent 2.2 min per hour on tasks related to medication reconciliation. We worry that patient safety related tasks in the EDs receive little attention. Allocating dedicated resources like pharmacists to contribute with medication-related tasks could benefit both physicians and patients.

Project description:IntroductionThe In-patient Medication Order Entry System (IPMOE) was first implemented in the medical ward of Princess Margaret Hospital, Hong Kong. It was a local developed close-loop system including prescription, dispensing and administration modules. Evaluation on its impact on nursing tasks would be important for practice improvement and subsequent system enhancement.ObjectiveThe study was conducted to quantify the nursing times across medication-associated tasks for paper-based MAR and computer-based IPMOE, including change in the tasks and time patterns before and after IPMOE implementation.MethodsThis was a prospective observation study in medical wards before (Jan 2014-Jun 2014) and after (Mar 2015-Jun 2015) the implementation of IPMOE. We conducted 8-hr observation studies of individual nurses with a customized application to time various pre-categorized nursing tasks. Statistical inferences and interrupted time series analysis was performed to identify the change in the intercept and trends over time after implementation.ResultThe average number of medication-related tasks was significantly reduced from 61.07 to 29.81, a reduction of 31.26 episodes per duty (P < 0.001, 95% CI 22.9-39.63). The time for the medication-related tasks was reduced from 32 min (SD = 21.57) to 26.57 min (SD = 11.35) and the medication administration time increased from 37.93 min (SD = 14.78) to 44.37 min (SD = 19.45), but there was no overall significant difference in the time spent on each duty (P = 0.315) between the two groups. An improving trend in the delayed effect was observed (P = 0.03), which indicated a run-in period for new application was needed in clinical setting.ConclusionOur study had shown the time motion observation could be applied to measure the impact of the IPMOE in a busy clinical setting. Through classification of activities, validation, objective measurement and longitudinal evaluation, the method could be applied in various systems as well as different clinical settings in measure efficiency.

Project description:CrowdsourcingCrowdsourcing is the practice of obtaining needed ideas, services, or content by requesting contributions from a large group of people. Amazon Mechanical Turk is a web marketplace for crowdsourcing microtasks, such as answering surveys and image tagging. We explored the limits of crowdsourcing by using Mechanical Turk for a more complicated task: analysis and creation of wind simulations.Harnessing crowdworkers for engineeringOur investigation examined the feasibility of using crowdsourcing for complex, highly technical tasks. This was done to determine if the benefits of crowdsourcing could be harnessed to accurately and effectively contribute to solving complex real world engineering problems. Of course, untrained crowds cannot be used as a mere substitute for trained expertise. Rather, we sought to understand how crowd workers can be used as a large pool of labor for a preliminary analysis of complex data.Virtual wind tunnelWe compared the skill of the anonymous crowd workers from Amazon Mechanical Turk with that of civil engineering graduate students, making a first pass at analyzing wind simulation data. For the first phase, we posted analysis questions to Amazon crowd workers and to two groups of civil engineering graduate students. A second phase of our experiment instructed crowd workers and students to create simulations on our Virtual Wind Tunnel website to solve a more complex task.ConclusionsWith a sufficiently comprehensive tutorial and compensation similar to typical crowd-sourcing wages, we were able to enlist crowd workers to effectively complete longer, more complex tasks with competence comparable to that of graduate students with more comprehensive, expert-level knowledge. Furthermore, more complex tasks require increased communication with the workers. As tasks become more complex, the employment relationship begins to become more akin to outsourcing than crowdsourcing. Through this investigation, we were able to stretch and explore the limits of crowdsourcing as a tool for solving complex problems.

Project description:PURPOSE:Robotic stabilization of a therapeutic radiation beam with respect to a dynamically moving tumor target can be accomplished either by moving the radiation source, the patient, or both. As the treatment beam is on during this process, the primary goal is to minimize exposure of normal tissue to radiation as much as possible when moving the target back to the desired position. Due to the complex mechanical structure of 6 degree-of-freedom (6DoF) robots, it is not intuitive as to what 6 dimensional (6D) correction trajectory is optimal in achieving such a goal. With proportional-integrative-derivative (PID) and other controls, the potential exists that the controller may generate a trajectory that is highly curved, slow, or suboptimal in that it leads to unnecessary exposure of healthy tissue to radiation. This work investigates a novel feedback planning method that takes into account a robot's mechanical joint structure, patient safety tolerances, and other system constraints, and performs real-time optimization to search the entire 6D trajectory space in each time cycle so it can respond with an optimal 6D correction trajectory. METHODS:Computer simulations were created for two 6DoF robotic patient support systems: a Stewart-Gough platform for moving a patient's head in frameless maskless stereotactic radiosurgery, and a linear accelerator treatment table for moving a patient in prostate cancer radiation therapy. Motion planning was formulated as an optimization problem and solved at real-time speeds using the L-BFGS algorithm. Three planning methods were investigated, moving the platform as fast as possible (platform-D), moving the target along a straight-line (target-S), and moving the target based on the fastest descent of position error (target-D). Both synthetic motion and prior recorded human motion were used as input data and output results were analyzed. RESULTS:For randomly generated 6D step-like and sinusoidal synthetic input motion, target-D planning demonstrated the smallest net trajectory error in all cases. On average, optimal planning was found to have a 45% smaller target trajectory error than platform-D control, and a 44% smaller target trajectory error than target-S planning. For patient head motion compensation, only target-D planning was able to maintain a ?0.5mm and ?0.5deg clinical tolerance objective for 100% of the treatment time. For prostate motion, both target-S planning and target-D planning outperformed platform-D control. CONCLUSIONS:A general 6D target trajectory optimization framework for robotic patient motion compensation systems was investigated. The method was found to be flexible as it allows control over various performance requirements such as mechanical limits, velocities, acceleration, or other system control objectives.

Dataset Information

Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps.

Publications

Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets