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Related Concept Videos

Somatosensation01:33

Somatosensation

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
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Sensory impulses related to touch, pressure, vibration, and proprioception from various body parts, such as the limbs, trunk, neck, and posterior head, travel to the cerebral cortex through the posterior column-medial lemniscus pathway. The pathway’s name derives from the two white-matter tracts that convey the impulses: the spinal cord's posterior column and the brainstem's medial lemniscus. First-order sensory neurons extend their axons into the spinal cord, forming the...
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NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation.

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Robots can now better understand objects during manipulation by combining vision and touch. This multimodal sensing improves spatial awareness, especially when vision is blocked, enhancing robotic dexterity.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision
  • Sensor Fusion

Background:

  • Human-level robotic dexterity requires sophisticated spatial awareness, particularly for in-hand manipulation of novel objects.
  • Current in-hand perception systems predominantly rely on vision, limiting their effectiveness with occluded objects and a priori unknown object models.
  • Visual occlusion is a significant challenge in robotic manipulation, hindering the development of robust in-hand manipulation capabilities.

Purpose of the Study:

  • To develop a multimodal sensing approach combining vision and touch for estimating object pose and shape during in-hand manipulation.
  • To overcome the limitations of vision-only systems, especially in scenarios with significant visual occlusion.
  • To advance robot dexterity by creating a robust perception backbone for in-hand object interaction.

Main Methods:

  • Introduced NeuralFeels, a method that encodes object geometry using an online learned neural field.
  • Jointly tracked object pose and shape by optimizing a pose graph problem, integrating vision and touch data.
  • Utilized a proprioception-driven policy for interaction with objects in both simulation and real-world experiments.

Main Results:

  • Achieved a final reconstruction F-score of 81% for object geometry.
  • Demonstrated an average pose drift of 4.7 millimeters, reduced to 2.3 millimeters with known object models.
  • Showcased significant improvements in tracking under heavy visual occlusion (up to 94% compared to vision-only methods).

Conclusions:

  • Multimodal sensing, integrating touch with vision, refines and disambiguates object perception during in-hand manipulation.
  • The NeuralFeels method provides a robust perception backbone for advancing robot dexterity, particularly in challenging occlusion scenarios.
  • The release of the FeelSight dataset aims to facilitate benchmarking and further research in multimodal in-hand perception.