A modular planar robotic manipulandum with end-point torque control.

Robotic manipulanda are extensively used in investigation of the motor control of human arm movements. They permit the application of translational forces to the arm based on its state and can be used to probe issues ranging from mechanisms of neural control to biomechanics. However, most current designs are optimized for studying either motor learning or stiffness. Even fewer include end-point torque control which is important for the simulation of objects and the study of tool use. Here we describe a modular, general purpose, two-dimensional planar manipulandum (vBOT) primarily optimized for dynamic learning paradigms. It employs a carbon fibre arm arranged as a parallelogram which is driven by motors via timing pulleys. The design minimizes the intrinsic dynamics of the manipulandum without active compensation. A novel variant of the design (WristBOT) can apply torques at the handle using an add-on cable drive mechanism. In a second variant (StiffBOT) a more rigid arm can be substituted and zero backlash belts can be used, making the StiffBOT more suitable for the study of stiffness. The three variants can be used with custom built display rigs, mounting, and air tables. We investigated the performance of the vBOT and its variants in terms of effective end-point mass, viscosity and stiffness. Finally we present an object manipulation task using the WristBOT. This demonstrates that subjects can perceive the orientation of the principal axis of an object based on haptic feedback arising from its rotational dynamics.

Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

Enhanced robotic body extension with modular units

The adaptation of robots to changing tasks has been explored in modular self-reconfigurable robot research, where the robot structure is altered by adapting the connectivity of its constituent modules. As these modules are generally complex and large, an upper bound is imposed on the resolution of the built structures. Inspired by growth of plants or animals, robotic body extension (RBE) based on hot melt adhesives allows a robot to additively fabricate and assemble tools, and integrate them into its own body. This enables the robot to achieve tasks which it could not achieve otherwise. The RBE tools are constructed from hot melt adhesives and therefore generally small and only passive. In this paper, we seek to show physical extension of a robotic system in the order of magnitude of the robot, with actuation of integrated body parts, while maintaining the ability of RBE to construct parts with high resolution. Therefore, we present an enhancement of RBE based on hot melt adhesives with modular units, combining the flexibility of RBE with the advantages of simple modular units. We explain the concept of this new approach and demonstrate with two simple unit types, one fully passive and the other containing a single motor, how the physical range of a robot arm can be extended and additional actuation can be added to the robot body. © 2012 IEEE.

Robotic body extension based on hot melt adhesives

The capability of extending body structures is one of the most significant challenges in the robotics research and it has been partially explored in self-reconfigurable robotics. By using such a capability, a robot is able to adaptively change its structure from, for example, a wheel like body shape to a legged one to deal with complexity in the environment. Despite their expectations, the existing mechanisms for extending body structures are still highly complex and the flexibility in self-reconfiguration is still very limited. In order to account for the problems, this paper investigates a novel approach to robotic body extension by employing an unconventional material called Hot Melt Adhesives (HMAs). Because of its thermo-plastic and thermo-adhesive characteristics, this material can be used for additive fabrication based on a simple robotic manipulator while the established structures can be integrated into the robot's own body to accomplish a task which could not have been achieved otherwise. This paper first investigates the HMA material properties and its handling techniques, then evaluates performances of the proposed robotic body extension approach through a case study of a "water scooping" task. © 2012 IEEE.

Transfer of dynamic learning across postures.

When learning a difficult motor task, we often decompose the task so that the control of individual body segments is practiced in isolation. But on re-composition, the combined movements can result in novel and possibly complex internal forces between the body segments that were not experienced (or did not need to be compensated for) during isolated practice. Here we investigate whether dynamics learned in isolation by one part of the body can be used by other parts of the body to immediately predict and compensate for novel forces between body segments. Subjects reached to targets while holding the handle of a robotic, force-generating manipulandum. One group of subjects was initially exposed to the novel robot dynamics while seated and was then tested in a standing position. A second group was tested in the reverse order: standing then sitting. Both groups adapted their arm dynamics to the novel environment, and this movement learning transferred between seated and standing postures and vice versa. Both groups also generated anticipatory postural adjustments when standing and exposed to the force field for several trials. In the group that had learned the dynamics while seated, the appropriate postural adjustments were observed on the very first reach on standing. These results suggest that the CNS can immediately anticipate the effect of learned movement dynamics on a novel whole-body posture. The results support the existence of separate mappings for posture and movement, which encode similar dynamics but can be adapted independently.

Microfabrication and application of reservoir pins for liquid transfer in biotechnology

Microarraying involves laying down genetic elements onto a solid substrate for DNA analysis on a massively parallel scale. Microarrays are prepared using a pin-based robotic platform to transfer liquid samples from microtitre plates to an array pattern of dots of different liquids on the surface of glass slides where they dry to form spots diameter < 200 μm. This paper presents the design, materials selection, micromachining technology and performance of reservoir pins for microarraying. A conical pin is produced by (i) conventional machining of stainless steel or wet etching of tungsten wire, followed by (ii) micromachining with a focused laser to produce a microreservoir and a capillary channel structure leading from the tip. The pin has a flat end diameter < 100 μm from which a 500 μm long capillary channel < 15 μm wide leads up the pin to a reservoir. Scanning electron micrographs of the metal surface show roughness on the scale of 10 μm, but the pins nevertheless give consistent and reproducible spotting performance. The pin capacity is 80 nanolitres of fluid containing DNA, and at least 50 spots can be printed before replenishing the reservoir. A typical robot holds can hold up to 64 pins. This paper discusses the fabrication technology, the performance and spotting uniformity for reservoir pins, the possible limits to miniaturization of pins using this approach, and the future prospects for contact and non-contact arraying technology.

Impedance control reduces instability that arises from motor noise.

There is ample evidence that humans are able to control the endpoint impedance of their arms in response to active destabilizing force fields. However, such fields are uncommon in daily life. Here, we examine whether the CNS selectively controls the endpoint impedance of the arm in the absence of active force fields but in the presence of instability arising from task geometry and signal-dependent noise (SDN) in the neuromuscular system. Subjects were required to generate forces, in two orthogonal directions, onto four differently curved rigid objects simulated by a robotic manipulandum. The endpoint stiffness of the limb was estimated for each object curvature. With increasing curvature, the endpoint stiffness increased mainly parallel to the object surface and to a lesser extent in the orthogonal direction. Therefore, the orientation of the stiffness ellipses did not orient to the direction of instability. Simulations showed that the observed stiffness geometries and their pattern of change with instability are the result of a tradeoff between maximizing the mechanical stability and minimizing the destabilizing effects of SDN. Therefore, it would have been suboptimal to align the stiffness ellipse in the direction of instability. The time course of the changes in stiffness geometry suggests that modulation takes place both within and across trials. Our results show that an increase in stiffness relative to the increase in noise can be sufficient to reduce kinematic variability, thereby allowing stiffness control to improve stability in natural tasks.

Multiple grasp-specific representations of tool dynamics mediate skillful manipulation.

Skillful tool use requires knowledge of the dynamic properties of tools in order to specify the mapping between applied force and tool motion. Importantly, this mapping depends on the orientation of the tool in the hand. Here we investigate the representation of dynamics during skillful manipulation of a tool that can be grasped at different orientations. We ask whether the motor system uses a single general representation of dynamics for all grasp contexts or whether it uses multiple grasp-specific representations. Using a novel robotic interface, subjects rotated a virtual tool whose orientation relative to the hand could be varied. Subjects could immediately anticipate the force direction for each orientation of the tool based on its visual geometry, and, with experience, they learned to parameterize the force magnitude. Surprisingly, this parameterization of force magnitude showed limited generalization when the orientation of the tool changed. Had subjects parameterized a single general representation, full generalization would be expected. Thus, our results suggest that object dynamics are captured by multiple representations, each of which encodes the mapping associated with a specific grasp context. We suggest that the concept of grasp-specific representations may provide a unifying framework for interpreting previous results related to dynamics learning.