Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

Abstract We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

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Self-powered soft robot in the Mariana Trench

Nature ◽

10.1038/s41586-020-03153-z ◽

2021 ◽

Vol 591 (7848) ◽

pp. 66-71

Author(s):

Guorui Li ◽

Xiangping Chen ◽

Fanghao Zhou ◽

Yiming Liang ◽

Youhua Xiao ◽

...

Keyword(s):

Soft Robot ◽

Mariana Trench ◽

Self Powered

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Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-020-01303-z ◽

2021 ◽

Vol 101 (3) ◽

Author(s):

Korbinian Nottensteiner ◽

Arne Sachtler ◽

Alin Albu-Schäffer

Keyword(s):

Autonomous Robots ◽

Vision System ◽

Dynamic Environments ◽

Bayesian Framework ◽

Experimental Results ◽

Robotic Assembly ◽

Tactile Sensing ◽

Task Execution ◽

Industrial Assembly ◽

Assembly Tasks

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

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