Learning dynamical systems with bifurcations

Khadivar, Farshad; Lauzana, Ilaria; Billard, Aude

doi:10.1016/j.robot.2020.103700

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…We found that mirroring the pitch orientation and not tracking the yaw rotation is more intuitive for subjects and simultaneously results in more accurate tracking. Then, the desired pose of the robot EE is passed to a predefined linear dynamical system (DS) [30] to find the desired translational and angular velocities. These velocities serve as the inputs for our underlying compliant and passive controller [31,32] that outputs a set of joint torques for the robotic arm.…”

Section: Robotic Arm Controlmentioning

confidence: 99%

EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses

et al. 2022

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Objective. The limited functionality of hand prostheses remains one of the main reasons behind the lack of its wide adoption by amputees. Indeed, while commercial prostheses can perform a reasonable number of grasps, they are often inadequate for manipulating the object once in hand. This lack of dexterity drastically restricts the utility of prosthetic hands. We aim at investigating a novel shared control strategy that combines autonomous control of forces exerted by a robotic hand with electromyographic (EMG) decoding to perform robust in-hand object manipulation. Approach. We conduct a 3-day long longitudinal study with 8 healthy subjects controlling a 16-degrees-of-freedom robotic hand to insert objects in boxes of various orientations. EMG decoding from forearm muscles enables subjects to move, proportionally and simultaneously, the fingers of the robotic hand. The desired object rotation is inferred using two EMG electrodes placed on the shoulder that record the activity of muscles responsible for elevation and depression. During the object interaction phase, the autonomous controller stabilizes and rotates the object to achieve the desired pose. In this study, we compare an incremental and a proportional shoulder-decoding method in combination with two state machine interfaces offering different levels of assistance. Main results. Results indicate that robotic assistance reduces the number of failures by $41\%$ and, when combined with an incremental shoulder EMG decoding, leads to faster task completion time (median=16.9s), compared to other control conditions. Training to use the assistive device is fast. After one session of practice, all subjects managed to achieve tasks with $50\%$ less failures. Significance. Shared control approaches that give some authority to an autonomous controller on-board the prosthesis are an alternative to control schemes relying on EMG decoding alone. This may improve the dexterity and versatility of robotic prosthetic hands (RPHs) for people with trans-radial amputation. By delegating control of forces to the prosthesis' on-board control, one speeds up reaction time and improves the precision of force control. Such a shared control mechanism may enable amputees to perform fine insertion tasks solely using their prosthetic hands. This may restore some of the functionality of the disabled arm.

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Section: Robotic Arm Controlmentioning

confidence: 99%

EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses

et al. 2022

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“…The dynamics cannot yet be accurate due to ubiquitous noises and disturbances. Many learning methods are presented to address it, such as reusing existed experience [21], employing an episodic method [11], exploiting Hopf bifurcations [18], recruiting recurrent spiking neuron networks [13], and so forth. The inverse dynamics also attracts attention in designing appropriate motion, and an example is the design of model-based controllers, which cancel out non-linearities and track with zero-error.…”

Section: A Robot Dynamics Learningmentioning

confidence: 99%

General Robot Dynamics Learning and Gen2Real

Xing,

Li,

Yang

et al. 2021

Preprint

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Acquiring dynamics is an essential topic in robot learning, but up-to-date methods, such as dynamics randomization, need to restart to check nominal parameters, generate simulation data, and train networks whenever they face different robots. To improve it, we novelly investigate general robot dynamics, its inverse models, and Gen2Real, which means transferring to reality. Our motivations are to build a model that learns the intrinsic dynamics of various robots and lower the threshold of dynamics learning by enabling an amateur to obtain robot models without being trapped in details. This paper achieves the "generality" by randomizing dynamics parameters, topology configurations, and model dimensions, which in sequence cover the property, the connection, and the number of robot links. A structure modified from GPT is applied to access the pretraining model of general dynamics. We also study various inverse models of dynamics to facilitate different applications. We step further to investigate a new concept, "Gen2Real", to transfer simulated, general models to physical, specific robots. Simulation and experiment results demonstrate the validity of the proposed models and method. 1

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“…Back-propagation through differential equation solvers has been a breakthrough over the past couple of years [16,17] that enabled scalable parameter inference for differential equations from trajectory data. Although one could use trajectory data to create the aforementioned qualitative constraints [18,19] this would entail over-constraining information originating from the kinetics and dynamical transients of the model. Furthermore, such data usually does not contain sufficient information about dynamical transients in order to identify kinetic parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter Inference with Bifurcation Diagrams

Szép¹,

Dalchau²,

Csikász‐Nagy³

2021

Preprint

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Estimation of parameters in differential equation models can be achieved by applying learning algorithms to quantitative time-series data. However, sometimes it is only possible to measure qualitative changes of a system in response to a controlled condition. In dynamical systems theory, such change points are known as bifurcations and lie on a function of the controlled condition called the bifurcation diagram. In this work, we propose a gradient-based semi-supervised approach for inferring the parameters of differential equations that produce a user-specified bifurcation diagram. The cost function contains a supervised error term that is minimal when the model bifurcations match the specified targets and an unsupervised bifurcation measure which has gradients that push optimisers towards bifurcating parameter regimes. The gradients can be computed without the need to differentiate through the operations of the solver that was used to compute the diagram. We demonstrate parameter inference with minimal models which explore the space of saddle-node and pitchfork diagrams and the genetic toggle switch from synthetic biology. Furthermore, the cost landscape allows us to organise models in terms of topological and geometric equivalence.Preprint. Under review.

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Learning dynamical systems with bifurcations

Cited by 10 publications

References 13 publications

EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses

EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses

General Robot Dynamics Learning and Gen2Real

Parameter Inference with Bifurcation Diagrams

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