Skip to content

QualityPoint Technologies News

Emerging Technologies News

Menu
  • About Us
  • Technology
  • Medical
  • Robots
  • Artificial Intelligence (AI)
  • 3D Printing
  • Contact Us
Menu

Huge Discount Offer: 14 ebooks + 2 courses

Researchers’ algorithm designs soft robots that sense

Posted on March 23, 2021

MIT researchers have developed a deep learning neural network to aid the design of soft-bodied robots.

There are some tasks that traditional robots — the rigid and metallic kind — simply aren’t cut out for. Soft-bodied robots, on the other hand, may be able to interact with people more safely or slip into tight spaces with ease. But for robots to reliably complete their programmed duties, they need to know the whereabouts of all their body parts. That’s a tall task for a soft robot that can deform in a virtually infinite number of ways.

MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot’s body, allowing it to better interact with its environment and complete assigned tasks. The advance is a step toward the automation of robot design. The system not only learns a given task, but also how to best design the robot to solve that task. The researcher says that sensor placement is a very difficult problem to solve. So, having this solution is extremely exciting.

The research will be presented during April’s IEEE International Conference on Soft Robotics and will be published in the journal IEEE Robotics and Automation Letters.

Creating soft robots that complete real-world tasks has been a long-running challenge in robotics. Their rigid counterparts have a built-in advantage: a limited range of motion. Rigid robots’ finite array of joints and limbs usually makes for manageable calculations by the algorithms that control mapping and motion planning. Soft robots are not so tractable.

Soft-bodied robots are flexible and pliant — they generally feel more like a bouncy ball than a bowling ball. The main problem with soft robots is that they are infinitely dimensional. Any point on a soft-bodied robot can, in theory, deform in any way possible. That makes it tough to design a soft robot that can map the location of its body parts. Past efforts have used an external camera to chart the robot’s position and feed that information back into the robot’s control program. But the researchers wanted to create a soft robot untethered from external aid.

The researchers developed a novel neural network architecture that both optimizes sensor placement and learns to efficiently complete tasks. First, the researchers divided the robot’s body into regions called “particles.” Each particle’s rate of strain was provided as an input to the neural network. Through a process of trial and error, the network “learns” the most efficient sequence of movements to complete tasks, like gripping objects of different sizes. At the same time, the network keeps track of which particles are used most often, and it culls the lesser-used particles from the set of inputs for the networks’ subsequent trials.

By optimizing the most important particles, the network also suggests where sensors should be placed on the robot to ensure efficient performance. For example, in a simulated robot with a grasping hand, the algorithm might suggest that sensors be concentrated in and around the fingers, where precisely controlled interactions with the environment are vital to the robot’s ability to manipulate objects. While that may seem obvious, it turns out the algorithm vastly outperformed humans’ intuition on where to site the sensors.

The researchers pitted their algorithm against a series of expert predictions. For three different soft robot layouts, the team asked roboticists to manually select where sensors should be placed to enable the efficient completion of tasks like grasping various objects. Then they ran simulations comparing the human-sensorized robots to the algorithm-sensorized robots. And the results weren’t close. The researchers says their model vastly outperformed humans for each task.

This work could help to automate the process of robot design.

Automating the design of sensorized soft robots is an important step toward rapidly creating intelligent tools that help people with physical task. The sensors are an important aspect of the process, as they enable the soft robot to “see” and understand the world and its relationship with the world.

News source: MIT

Share

Related News:

  1. Jellyfish-inspired soft robots can outswim their natural counterparts
  2. Self-folding nanotech creates world’s smallest origami bird
  3. Scientists Create the Next Generation of Living Robots
  4. Soft assistive robotic wearables get a boost from rapid design tool 
Master RAG ⭐ Rajamanickam.com ⭐ Bundle Offer ⭐ Merch ⭐ AI Course

  • Bundle Offer
  • Hire AI Developer

Latest News

  • ​Firebase Studio: Google’s New Platform for Building AI-Powered Applications April 11, 2025
  • MIT Researchers Develop Framework to Enhance LLMs in Complex Planning April 7, 2025
  • MIT and NVIDIA Unveil HART: A Breakthrough in AI Image Generation March 25, 2025
  • Can LLMs Truly Understand Time Series Anomalies? March 18, 2025
  • Can AI tell us if those Zoom calls are flowing smoothly? March 11, 2025
  • New AI Agent, Manus, Emerges to Bridge the Gap Between Conception and Execution March 10, 2025
  • OpenAI Unveils GPT-4.5, Promising Enhanced AI Performance February 28, 2025
  • Anthropic Launches Claude Code to Revolutionize Developer Productivity February 25, 2025
  • Google Unveils Revolutionary AI Co-Scientist! February 24, 2025
  • Microsoft’s Majorana 1 Chip: Revolutionizing Quantum Computing with Topological Core Architecture February 20, 2025

Pages

  • About Us
  • Basics of 3D Printing
  • Key Innovations
  • Know about Graphene
  • Privacy Policy
  • Shop
  • Contact Us

Archives

Developed by QualityPoint Technologies (QPT)

QPT Products | eBook | Privacy

Timesheet | Calendar Generator

©2025 QualityPoint Technologies News | Design: Newspaperly WordPress Theme
Menu
  • About Us
  • Technology
  • Medical
  • Robots
  • Artificial Intelligence (AI)
  • 3D Printing
  • Contact Us