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NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation.

Science roboticsยท2024
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Related Experiment Video

Updated: May 6, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
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Within arm's reach: A path forward for robot dexterity.

Sudharshan Suresh1

  • 1Boston Dynamics Inc., Waltham, MA, USA.

Science Robotics
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Visuotactile pretraining using human data enhances robotic manipulation skills. This approach allows for robust policies to be trained effectively in simulation environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision
  • Haptics

Background:

  • Robotic manipulation requires sophisticated control policies.
  • Training these policies often relies on extensive real-world data or complex simulations.
  • Bridging the gap between simulation and real-world performance remains a challenge.

Purpose of the Study:

  • To investigate the effectiveness of visuotactile pretraining for developing robust manipulation policies.
  • To leverage human demonstration data for improving simulation-based training.

Main Methods:

  • Utilized a visuotactile pretraining strategy incorporating human interaction data.
  • Trained manipulation policies within a simulated environment.

Main Results:

  • Visuotactile pretraining significantly improved the robustness of learned manipulation policies.
  • Policies trained with this method demonstrated enhanced performance compared to traditional simulation-only approaches.

Conclusions:

  • Pretraining with human visuotactile data is a viable method for creating more capable robotic manipulation systems.
  • Simulation-based training can be made more effective by incorporating real-world human interaction data.