Robotic Primitives Research Articles

Shape Programming Using Triangular and Rectangular Soft Robot Primitives.

This paper presents fabric-based soft robotic modules with primitive morphologies, which are analogous to basic geometrical polygons—trilateral and quadrilateral. The two modules are the inflatable beam (IB) and fabric-based rotary actuator (FRA). The FRA module is designed with origami-inspired V-shaped pleats, which creates a trilateral outline. Upon pressurization, the pleats unfold, which enables propagation of angular displacement of the FRA module. This allows the FRA module to be implemented as a mobility unit in the larger assembly of pneumatic structures. In the following, we examine various ways by which FRA modules can be connected to IB modules. We studied how different ranges of motion can be achieved by varying the design of the rotary joint of the assemblies. Using a state transition-based position control system, movement of the assembled modules could be controlled by regulating the pneumatic pressurization of the FRA module at the joint. These basic modules allow us to build different types of pneumatic structures. In this paper, using IB and FRA modules of various dimensions, we constructed a soft robotic limb with an end effector, which can be attached to wheelchairs to provide assistive grasping functions for users with disabilities.

A Non-Newtonian Fluid Robot.

New types of robots inspired by biological principles of assembly, locomotion, and behavior have been recently described. In this work we explored the concept of robots that are based on more fundamental physical phenomena, such as fluid dynamics, and their potential capabilities. We report a robot made entirely of non-Newtonian fluid, driven by shear strains created by spatial patterns of audio waves. We demonstrate various robotic primitives such as locomotion and transport of metallic loads-up to 6-fold heavier than the robot itself-between points on a surface, splitting and merging, shapeshifting, percolation through gratings, and counting to 3. We also utilized interactions between multiple robots carrying chemical loads to drive a bulk chemical synthesis reaction. Free of constraints such as skin or obligatory structural integrity, fluid robots represent a radically different design that could adapt more easily to unfamiliar, hostile, or chaotic environments and carry out tasks that neither living organisms nor conventional machines are capable of.

Understanding Human Hand Gestures for Learning Robot Pick-and-Place Tasks

Programming robots by human demonstration is an intuitive approach, especially by gestures. Because robot pick-and-place tasks are widely used in industrial factories, this paper proposes a framework to learn robot pick-and-place tasks by understanding human hand gestures. The proposed framework is composed of the module of gesture recognition and the module of robot behaviour control. For the module of gesture recognition, transport empty (TE), transport loaded (TL), grasp (G), and release (RL) from Gilbreth's therbligs are the hand gestures to be recognized. A convolution neural network (CNN) is adopted to recognize these gestures from a camera image. To achieve the robust performance, the skin model by a Gaussian mixture model (GMM) is used to filter out non-skin colours of an image, and the calibration of position and orientation is applied to obtain the neutral hand pose before the training and testing of the CNN. For the module of robot behaviour control, the corresponding robot motion primitives to TE, TL, G, and RL, respectively, are implemented in the robot. To manage the primitives in the robot system, a behaviour-based programming platform based on the Extensible Agent Behavior Specification Language (XABSL) is adopted. Because the XABSL provides the flexibility and re-usability of the robot primitives, the hand motion sequence from the module of gesture recognition can be easily used in the XABSL programming platform to implement the robot pick-and-place tasks. The experimental evaluation of seven subjects performing seven hand gestures showed that the average recognition rate was 95.96%. Moreover, by the XABSL programming platform, the experiment showed the cube-stacking task was easily programmed by human demonstration.

Learning behavior fusion from demonstration

A critical challenge in robot learning from demonstration is the ability to map the behavior of the trainer onto a robot’s existing repertoire of basic/primitive capabilities. In part, this problem is due to the fact that the observed behavior of the teacher may consist of a combination (or superposition) of the robot’s individual primitives. The problem becomes more complex when the task involves temporal sequences of goals. We introduce an autonomous control architecture that allows for learning of hierarchical task representations, in which: (1) every goal is achieved through a linear superposition (or fusion) of robot primitives and (2) sequencing across goals is achieved through arbitration. We treat learning of the appropriate superposition as a state estimation problem over the space of possible linear fusion weights, inferred through a particle filter. We validate our approach in both simulated and real world environments with a Pioneer 3DX mobile robot.

Vision-based urban navigation procedures for verbally instructed robots

When humans explain a task to be executed by a robot they decompose it into chunks of actions. These form a chain of search-and-act sensory-motor loops that exit when a condition is met. In this paper we investigate the nature of these chunks in an urban visual navigation context, and propose a method for implementing the corresponding robot primitives such as “take the nth turn right/left”. These primitives make use of a “short-lived” internal map updated as the robot moves along. The recognition and localisation of intersections is done in the map using task-guided template matching. This approach takes advantage of the content of human instructions to save computation time and improve robustness.

Primitives for robotic mechanical assembly: Force directed insertions

Definition of robotic primitives for insertions is an essential step in achieving a truly flexible manufacturing environment. We present techniques based on active compliance implemented with hybrid force-position control capable of inserting a wide variety of shaped pegs. Analysis of this method indicates that it will work on arbitrarily shaped convex pegs, and may frequently work on non-convex pegs as well. The technique presented here provides a significant step toward simplifying the programming of a flexible manufacturing environment.

Technology for Robotic Mechanical Assembly: Force-Directed Insertions

Definition of robotic primitives for insertions is an essential step in achieving a truly flexible manufacturing environment. We present techniques based on active compliance, implemented with hybrid force-position control, that are capable of inserting a wide variety of shaped pegs. We analyze the forces encountered during the insertions to determine what classes of shapes these techniques will consistently insert. The analysis also gives guidance in selecting parameters of these techniques for specific shapes. These techniques provide a significant step toward simplifying the programming of a flexible manufacturing environment.