Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

A novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system specific modifications of the DEP rule but arise rather from the underlying mechanism of spontaneous symmetry breaking due to the tight brain-body-environment coupling. The new synaptic rule is biologically plausible and it would be an interesting target for a neurobiolocal investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

The application of human motor control principles to a collective robotic arm

Current robots are no match for biological organisms when adapting to real-world, dynamic environments. Collective control strategies, such as those used by synergistic biological systems composed of large numbers of identical parts like the human nervous system, provide a novel and alternative approach for the design of fault-tolerant, adaptable robotic systems that have traditionally relied on centralized control. In this research, a robotic arm composed of multiple identical segments in a collective computational architecture was tested for its ability to produce adaptive pointing and reaching behavior. The movement rules for these robotic arm segments were based on the concepts of the "reflex arc" and the "action system" in the human nervous system. Robotic arms of three to seven encapsulated segments were tested. These arms received no central directions and used no direct informational exchange. The arms were sensor-driven at their distal, or leading, outstretched ends to maximize pointing accuracy on a two-dimensional target plane. The remaining non-distal segments in the arms were moved in a sequential order using sensed locally-available movement information about neighboring segments. Successful pointing and reaching behavior was observed in situations with and without movement obstacles. This led to the conclusion that because such behavior was not specified within each segment, the overall arm behavior emerged due to the interaction and coordination of all segments, rather than due to any single segment, centrally-controlled influence, or explicit inter-segmental method of communication

TERMES: An Autonomous Robotic System for Three-Dimensional Collective Construction

Collective construction is the research area in which autonomous multi-robot systems build structures according to user specifications. Here we present a hardware system and high-level control scheme for autonomous construction of 3D structures under conditions of gravity. The hardware comprises a mobile robot and specialized passive blocks; the robot is able to manipulate blocks to build desired structures, and can maneuver on these structures as well as in unstructured environments. We describe and evaluate the robot's key capabilities of climbing, navigation, and manipulation, and demonstrate its ability to perform complex tasks that combine these capabilities by having it autonomously build a ten-block staircase taller than itself. In addition, we outline a simple decentralized control algorithm by which multiple simultaneously active robots could autonomously build user-specified structures, working from a high-level description as input.Engineering and Applied Science

Algorithms in nature: the convergence of systems biology and computational thinking

Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. This Perspectives discusses the recent convergence of these two ways of thinking

Neuromorphic electronics for real-time biomedical image processing

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Evolutionary Distributed Control of a Biologically Inspired Modular Robot

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