A Cerebellar Internal Models Control Architecture for Online Sensorimotor Adaptation of a Humanoid Robot Acting in a Dynamic Environment
Summary (2 min read)
Introduction
- Of artificial neural networks (ANNs) into nonlinear dynamical systems adaptive control were advantageous for reducing the effects of nonlinearities and uncertainties, and for handling high dimensional and continuous state space systems [11], [12], [13], [8], [14].
- The structure of the paper is as follows: in section II the authors describe the overall control architecture, giving special focus to the cerebellar-like component; in section III, the experimental set up and results are presented.
A. Robot Plant
- The humanoid iCub is a 53 degree of freedom (dof) robotic system equipped with several type of sensors, such as: encoders, accelerometers, gyroscopes, F/T sensors, digital cameras.
- For the sake of simplicity, the overall system actuates seven motors of the right arm: four motors are kept constant to keep the arm upwards (i.e. elbow, shoulder roll, shoulder yaw and shoulder pitch), and N = 3 motors are controlled by 2377-3766 (c) 2019 IEEE.
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- The n-th actual motor state is read by the encoders and saved in the qn ∈ QN×1 angular position and q̇n ∈ Q̇N×1 angular velocity process variables.
C. Controller
- The Controller once received the Q, Q̇ actual robot states computes the τntot ∈ τ totN×1 torque command to move each actuator to the qrn,q̇ r n desired state.
- This subsystem is constituted by a static module based on classical control methods, and by two decentralized cerebellar-like neural networks (section II-D): inverse and forward models (blue boxes Fig.2.b).
- The forward model corrective term is narrowed to the angular velocity, which is the feedback controller input.
- (3) This quantity is corrected by the forward cerebellar-like module which predicts the consequence of the outgoing motor command and adds ∆q̇cn contribution to minimize the e fb n feedback error.
D. Cerebellar-like Network
- The cerebellum is constituted of several micro-zones that plausibly correspond to the minimal ulm unit learning machine (Fig.3) [63].
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- Cells, that in Marr’s opinion encode combinations of mossy fibers inputs [20]; the pc Purkinje cells (in green Fig.3), that modulated by the inferior olive axon and excited by the pf parallel fibers (in violet) projecting from the granule cells, they influence the activity of the dcn deep cerebellar nuclei (in blue).
- These models are employed by the algorithm to make τ̂grn,g , ˆ̇q gr n,g local predictions of the control input (inverse MCC) and angular velocity (forward MCC) respectively.
III. RESULTS
- Four architectures that differ in terms of internal models contributions are compared: (I) feedback controller; (II) feedback controller combined with inverse cerebellar-like network; (III) feedback controller combined with forward cerebellarlike network; (IV) feedback controller combined with inverse and forward cerebellar-like networks.
- Due to the stochastic nature of the experiments, the recorded data are expressed as µ mean value and σ standard deviation of the 20 tests.
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- In particular, thanks to the forward model action, architectures III and IV robustly reduce the effect of noise as suggested by [51].
- It is worthwhile to mention that the feedback controller of the first joint is highly affected by the table weight, which slowly leads the joint towards the correct reference.
IV. CONCLUSIONS
- Thus far, the authors have presented, tested, and compared four control architectures based on a versatile and real-time modeling structure that replicates the cerebellar internal models individual and combinatorial theories.
- The experiments confirmed the theories about the internal model independent and combinatorial contribution.
- In the proposed model, the learning rules that iteratively update the network weights are based on synaptic plasticities derived from computational neuroscience studies [42], [59].
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- At the current state the cerebellar network can not generalize all the possible conditions.
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References
254 citations
"A Cerebellar Internal Models Contro..." refers methods in this paper
...To replicate the efference copy theory [39], [71], the LWPR uses a copy of the outgoing τ tot (inverse MCC) and actual q̇ (forward MCC) as modulatory signals (in cyan Fig....
[...]
250 citations
"A Cerebellar Internal Models Contro..." refers methods in this paper
...Inspired by the theory of coupled internal models [53], [54], [55], [56], [57], [58], we propose a novel methodology to replicate and exploit artificially the cerebellar internal models learning and corrective action....
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241 citations
"A Cerebellar Internal Models Contro..." refers background in this paper
...If confirmed, these assumptions would explain several complex mechanisms underlying the neural control of movements [34]....
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..., quick ballistic limbs movements and impaired muscle coordination [36], are due to the lack of feed forward contribution in motor control, or rather the neural control loop is affected by slow reaction time and sensory delay [34]....
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188 citations
Additional excerpts
...The inverse cerebellar-like module adds Δτ c n ∈ Δτ N×1 feed-forward corrective torque command to the τ n ,∈ τ fb N×1 feedback controller motor input [63], [64], while the forward module applies Δq̇ n ∈ Δq̇ state-specific adjustment to the feedback loop [58], [65], [66]....
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