Performance Of Manipulation Tasks Research Articles

Geometry-aware manipulability learning, tracking, and transfer

Body posture influences human and robot performance in manipulation tasks, as appropriate poses facilitate motion or the exertion of force along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control, and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or applying a specific force. In this context, this article presents a novel manipulability transfer framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive-definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.

Effects of muscle fatigue on grip and load force coordination and performance of manipulation tasks

Muscle fatigue is known to be associated with a deteriorated muscle coordination and impaired movement performance in variety of voluntary movements. The aim of this study was to investigate the generally underexplored effect of muscle fatigue on both the coordination between grip force (GF; the force component perpendicular to the hand–object contact area that provides friction) and load force (LF; the parallel force component that can move the object or support the body) as well as movement performance in manipulation tasks. Fifteen participants performed a variety of static and dynamic manipulations both with and without a preceding procedure designed to fatigue the arm and hand muscles. The tasks involved exertion of ramp-and-hold and oscillation patterns of LF against an externally fixed instrumented device, and a simple lift of a freely moving device. The results revealed a fatigue-associated decrease in GF scaling (i.e., the magnitude of GF relative to LF) and GF–LF coupling (correlation between GF and LF), while the task performance regarding the accuracy of exertion of the prescribed LF profiles remained unaffected. We conclude that muscle fatigue both partly decouples GF from LF and reduces the overall GF magnitude, which could potentially explain why hand-held objects are more likely to drop when manipulated with fatigued muscles. However, the unaffected task performance could be explained either by the relatively low level of muscle forces required by the tested tasks, the moderate level of the fatigue imposed, or both.

Latency Discrimination Mechanisms in Virtual Environments: Velocity and Displacement Error Factors

Latency (time delay) in head-tracked virtual environments (VEs) is well known to disturb users' sense of presence as well as hinder performance in manipulation tasks. We have determined in previous studies that observers cannot rely solely on direct temporal detection of time delay. They, at least in part, perceive the consequent visual “slip” of the VE scene from its expected spatially stable location. By employing an occlusion technique, the present experiment enabled comparison of the influence of visual image displacement and velocity errors on observer discrimination of VE system latency when using head-mounted displays. The results show better latency sensitivity (i.e., lower Just Noticeable Differences, or JNDs) in situations where velocity error magnitude predominates over displacement error cues, even though the velocity errors were visible for shorter durations. Correlation analyses indicate that latency-induced velocity errors are most closely related to latency JND than displacement errors.

An Expectation-Based Framework of Object Schemas and Port-Based Agents for Disparate Feedback Assimilation

Robotic manipulation systems that operate in unstructured environments must be responsive to feedback from sensors that are disparate in both location and modality. This paper describes a distributed framework for assimilating the disparate feedback provided by force and vision sensors, including active vision sensors, for robotic manipulation systems. The main components of the expectation-based framework include object schemas and port-based agents. Object schemas represent the manipulation task internally in terms of geometric models with attached sensor mappings. Object schemas are dynamically updated by sensor feedback, and thus provide an ability to perform three dimensional spatial reasoning during task execution. Because object schemas possess knowledge of sensor mappings, they are able to both select appropriate sensors and guide active sensors based on task characteristics. Port-based agents are the executors of reference inputs provided by object schemas and are defined in terms of encapsulated control strategies. Experimental results demonstrate the capabilities of the framework in two ways: the performance of manipulation tasks with active camera-lens systems, and the assimilation of force and vision sensory feedback.

Introduction to the special issue: Intelligent control for manipulation

This special issue is concerned with the intelligent control of robots in the performance of manipulation tasks. First we will try to define what we mean by intelligent control and by manipulation tasks.Unfortunately, intelligent control is a somewhat imprecise term, just like the word control itself, so that its meaning can vary widely depending on the application and on the writer's scientific discipline. In general terms, we can say that intelligent control implies some degree of autonomy in performing a task while accommodating uncertainty. In the control of sensor-based robot systems, we could characterize an intelligent controller as one with the following capabilities;