Learning Global Inverse Kinematics Solutions for a Continuum Robot

Thuruthel, Thomas George; Falotico, Egidio; Cianchetti, Matteo; Laschi, Cecilia

doi:10.1007/978-3-319-33714-2_6

Cited by 38 publications

(21 citation statements)

References 11 publications

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“…Another technique for learning the IK was proposed in Refs., 46,47 where the IK problem is formulated like a differential IK problem using local mappings. This allowed for redundancy resolution as well as reducing stochastic effects.…”

Section: Model-free Static Controllersmentioning

confidence: 99%

“…This allowed for redundancy resolution as well as reducing stochastic effects. However, the approach was validated only by simulations on a continuum 46 and soft arm. 47 Another advantage of such an approach is that it allows multiple solutions to the IK problem globally and can work even if some of the actuators are nonfunctional after the learning process.…”

Section: Model-free Static Controllersmentioning

confidence: 99%

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Control Strategies for Soft Robotic Manipulators: A Survey

et al. 2018

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With the rise of soft robotics technology and applications, there have been increasing interests in the development of controllers appropriate for their particular design. Being fundamentally different from traditional rigid robots, there is still not a unified framework for the design, analysis, and control of these high-dimensional robots. This review article attempts to provide an insight into various controllers developed for continuum/soft robots as a guideline for future applications in the soft robotics field. A comprehensive assessment of various control strategies and an insight into the future areas of research in this field are presented.

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Section: Model-free Static Controllersmentioning

confidence: 99%

Section: Model-free Static Controllersmentioning

confidence: 99%

Control Strategies for Soft Robotic Manipulators: A Survey

et al. 2018

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“…Additionally, George Thuruthel et al (2018) mention nonlinear material effects such as compliance, visco-elastic material behaviors, and hysteresis, as well as the wide range of design and actuation techniques that account for the non-trivial nature of this problem. Previous works have particularly studied the problem of inverse kinematics (IK) which is concerned with finding a mapping between actuator configuration and desired hand configuration (i.e., pose) (Rolf and Steil, 2013;George Thuruthel et al, 2016;Jiang et al, 2017;Schlagenhauf et al, 2018;Bauer et al, 2020). Existing control approaches can be classified into three main categories: model-based or model-free controllers, as well as combinations of both.…”

Section: Related Workmentioning

confidence: 99%

Characterizing Continuous Manipulation Families for Dexterous Soft Robot Hands

2021

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There has been an explosion of ideas in soft robotics over the past decade, resulting in unprecedented opportunities for end effector design. Soft robot hands offer benefits of low-cost, compliance, and customized design, with the promise of dexterity and robustness. The space of opportunities is vast and exciting. However, new tools are needed to understand the capabilities of such manipulators and to facilitate manipulation planning with soft manipulators that exhibit free-form deformations. To address this challenge, we introduce a sampling based approach to discover and model continuous families of manipulations for soft robot hands. We give an overview of the soft foam robots in production in our lab and describe novel algorithms developed to characterize manipulation families for such robots. Our approach consists of sampling a space of manipulation actions, constructing Gaussian Mixture Model representations covering successful regions, and refining the results to create continuous successful regions representing the manipulation family. The space of manipulation actions is very high dimensional; we consider models with and without dimensionality reduction and provide a rigorous approach to compare models across different dimensions by comparing coverage of an unbiased test dataset in the full dimensional parameter space. Results show that some dimensionality reduction is typically useful in populating the models, but without our technique, the amount of dimensionality reduction to use is difficult to predict ahead of time and can depend on the hand and task. The models we produce can be used to plan and carry out successful, robust manipulation actions and to compare competing robot hand designs.

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“…Hence, Goal Babbling was proposed and inspired by infant motor learning skills for direct learning of IK within a few 100 samples (Rolf et al, 2010 , 2011 ). Various other schemes were proposed for learning IK e.g., direct learning of IK (D'Souza et al, 2001 ; Thuruthel et al, 2016a ) and incremental learning of IK (Vijayakumar et al, 2005 ; Baranes and Oudeyer, 2013 ).…”

Section: Related Workmentioning

confidence: 99%

Learning Inverse Statics Models Efficiently With Symmetry-Based Exploration

2018

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Learning (inverse) kinematics and dynamics models of dexterous robots for the entire action or observation space is challenging and costly. Sampling the entire space is usually intractable in terms of time, tear, and wear. We propose an efficient approach to learn inverse statics models—primarily for gravity compensation—by exploring only a small part of the configuration space and exploiting the symmetry properties of the inverse statics mapping. In particular, there exist symmetric configurations that require the same absolute motor torques to be maintained. We show that those symmetric configurations can be discovered, the functional relations between them can be successfully learned and exploited to generate multiple training samples from one sampled configuration-torque pair. This strategy drastically reduces the number of samples required for learning inverse statics models. Moreover, we demonstrate that exploiting symmetries for learning inverse statics models is a generally applicable strategy for online and offline learning algorithms. We exemplify this by two different learning approaches. First, we modify the Direction Sampling approach for learning inverse statics models online, in a plain exploratory fashion, from scratch and without using a closed-loop controller. Second, we show that inverse statics mappings can be efficiently learned offline utilizing lattice sampling. Results for a 2R planar robot and a 3R simplified human arm demonstrate that their inverse statics mappings can be learned successfully for the entire configuration space. Furthermore, we demonstrate that the number of samples required for learning inverse statics mappings for 2R and 3R manipulators can be reduced at least by factors of approximately 8 and 16, respectively–depending on the number of discovered symmetries.

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Learning Global Inverse Kinematics Solutions for a Continuum Robot

Cited by 38 publications

References 11 publications

Control Strategies for Soft Robotic Manipulators: A Survey

Control Strategies for Soft Robotic Manipulators: A Survey

Characterizing Continuous Manipulation Families for Dexterous Soft Robot Hands

Learning Inverse Statics Models Efficiently With Symmetry-Based Exploration

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