41 research outputs found
The implications of embodiment for behavior and cognition: animal and robotic case studies
In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5
Morphological properties of mass-spring networks for optimal locomotion learning
Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size
Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective
Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perceptionThis study was supported by the European Commission with the RoboSoft CA (A Coordination Action for Soft Robotics, contract #619319).
SGN was supported by School of Engineering seed funding (2016), Malaysia Campus, Monash University
Model-free Soft-Structure Reconstruction for Proprioception using Tactile Arrays
Continuum body structures provide unique opportunities for soft robotics, with the infinite degrees of freedom
enabling unconstrained and highly adaptive exploration and manipulation. However, the infinite degrees of freedom of continuum
bodies makes sensing (both intrinsically and extrinsically) challenging. To address this, in this paper we propose a model-free
method for sensorizing tentacle-like continuum soft-structures
using an array of spatially arranged capacitive tactile sensors.
By using visual tracking, the relationship between the tactile
response and the 3D shape of the continuum soft-structure can be
learned. A data set of 15000 random soft-body postures was used,
with recorded camera-tracked positions logged synchronously to
the tactile sensor responses. This was used to train a neural
network which can predict posture. We show it is possible to
achieve proprioceptive awareness over all three axis of motion
in space, reconstructing the body structure and inferring the
soft body head’s pose with an average accuracy of ≈ 1mm in
comparison to the visual tracked counterpart. To demonstrate
the capabilities of the system, we perform random exploration
of environments limiting the work-space of the sensorized robot.
We find the method capable to autonomously reconstruct the
reachable morphology of the environment without the need of
external sensing units.This work was funded by the UK Agriculture and Horticulture Development
Board (CP 172) and Physical Sciences Research Council (EPSRC) MOTION
grant [EP/N03211X/2
A new mechanical design for legged robots to reduce energy consumption
Many legged robots have been designed and built by universities, research institutes and industry; however, few investigations regard energy consumption as a crucial design criterion. This paper presents a novel configuration for legged robots to reduce the energy consumption. The proposed leg can be either used as a single leg or easily attached to bodies with four, six and eight legs. This mechanism is a parallel four-bar linkage equipped with one active and four passive joints. In fact, the usage of the passive elements leads to simple feed-forward control paradigms. Moreover, another distinctive feature of this design is the arrangement of one-way clutches and flat springs to store the potential energy for utilizing it in the next step. A locomotion prototype of the proposed mechanical structure is built and its simulation is also presented in this paper. Comparing the results with other structures demonstrates the superiority and efficiency of this work regarding energy consumption problem.</p
A Model-based Hierarchical Controller for Legged Systems subject to External Disturbances
Xin G, Lin H-C, Smith J, Cebe O, Mistry M. A Model-based Hierarchical Controller for Legged Systems subject to External Disturbances. In: IEEE/RSJ Int. Conf. on Robotics and Automation. 2018.Legged robots have many potential applications
in real-world scenarios where the tasks are too dangerous for
humans, and compliance is needed to protect the system against
external disturbances and impacts. In this paper, we propose a
model-based controller for hierarchical tasks of legged systems
subject to external disturbance. The control framework is
based on projected inverse dynamics controller, such that the
control law is decomposed into two orthogonal subspaces,
i.e., the constrained and the unconstrained subspaces. The
unconstrained component controls multiple desired tasks with
impedance responses. The constrained space controller maintains
the contact subject to unknown external disturbances,
without the use of any force/torque sensing at the contact
points. By explicitly modelling the external force, our controller
is robust to external disturbances and errors arising from
incorrect dynamic model information. The main contributions
of this paper include (1) incorporating an impedance controller
to control external disturbances and allow impedance shaping
to adjust the behaviour of the motion under external disturbances,
(2) optimising contact forces within the constrained
subspace that also takes into account the external disturbances
without using force/torque sensors at the contact locations. The
techniques are evaluated on the ANYmal quadruped platform
under a variety of scenarios