Amazon and University of Michigan give robots a sense of touch
Captured source
source ↗How HydroShear gives robots a realistic sense of touch, trained entirely in simulation - Amazon Science
Close
Close
Social
bluesky
threads
youtube
github
rss
Menu
Research
Research areas
Automated reasoning
Cloud and systems
Computer vision
Conversational AI
Economics
Information and knowledge management
Machine learning
Operations research and optimization
Quantum technologies
Robotics
Search and information retrieval
Security, privacy, and abuse prevention
Sustainability
Our scientific contributions
Publications
Research from our scientists and collaborators.
Conferences
Our experts present and discuss cutting-edge research at scientific meetings globally.
Research areas
Automated reasoning
Cloud and systems
Computer vision
Conversational AI
Economics
Information and knowledge management
Machine learning
Operations research and optimization
Quantum technologies
Robotics
Search and information retrieval
Security, privacy, and abuse prevention
Sustainability
Our scientific contributions
Publications
Research from our scientists and collaborators.
Conferences
Our experts present and discuss cutting-edge research at scientific meetings globally.
News & blog
The latest from Amazon researchers
Amazon Science Blog
Technical deep-dives and perspectives from our scientists.
News
Research milestones and recent achievements.
The latest from Amazon researchers
Amazon Science Blog
Technical deep-dives and perspectives from our scientists.
News
Research milestones and recent achievements.
Collaborations
Amazon Research Awards
Overview
Call for proposals
Latest news
Research stories
Recipients
Amazon Nova AI Challenge
Overview
Rules
FAQs
Teams
Research collaborations
Overview
Carnegie Mellon University
Columbia University
Hampton University
Howard University
IIT Bombay
Johns Hopkins University
Max Planck Society
MIT
Tennessee State University
University of California, Los Angeles
University of Illinois Urbana-Champaign
University of Southern California
University of Texas at Austin
Virginia Tech
University of Washington
Amazon Research Awards
Overview
Call for proposals
Latest news
Research stories
Recipients
Amazon Nova AI Challenge
Overview
Rules
FAQs
Teams
Research collaborations
Overview
Carnegie Mellon University
Columbia University
Hampton University
Howard University
IIT Bombay
Johns Hopkins University
Max Planck Society
MIT
Tennessee State University
University of California, Los Angeles
University of Illinois Urbana-Champaign
University of Southern California
University of Texas at Austin
Virginia Tech
University of Washington
Resources
Code and datasets
AGI Labs
Meet the team building useful AI agents.
Amazon Nova
Try Amazon’s frontier foundation models.
Code and datasets
AGI Labs
Meet the team building useful AI agents.
Amazon Nova
Try Amazon’s frontier foundation models.
Careers
Careers
Explore our open roles.
Amazon Scholars
Faculty research opportunities on industry-scale technical challenges.
Postdoctoral Science Program
Early-career research opportunities alongside experienced industry scientists.
Careers
Explore our open roles.
Amazon Scholars
Faculty research opportunities on industry-scale technical challenges.
Postdoctoral Science Program
Early-career research opportunities alongside experienced industry scientists.
Search
Submit Search
Robotics
Amazon and University of Michigan give robots a sense of touch
HydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world.
By Mani Nambi , Nima Fazeli
July 10, 2026
5 min read
Share
Share
Copy link
X
Line
QZone
Sina Weibo
分享到微信
x
Key takeaways
HydroShear introduces path-dependent force tracking in hydroelastic contact models to accurately simulate tactile forces, enabling robots to learn dexterous manipulation policies in simulation. The simulator achieves a 93% average success rate across four challenging tasks when deployed on a real Franka robot with GelSight Mini sensors, outperforming baselines TacSL (34%) and FOTS (58-61%). HydroShear's approach allows for efficient, large-scale policy training in simulation, significantly reducing the need for real-world data collection and trial-and-error learning.
Was this answer helpful?
From warehouse automation to surgical assistance, many real-world applications depend on robots performing delicate, contact-intensive tasks. Often missing in these situations is the sense of touch: robots need to feel the forces on their fingertips to manipulate objects effectively. Despite years of effort, robust and scalable solutions to this problem remain out of reach, especially in industrial settings. One approach has been to use vision-based tactile sensors, in which cameras embedded in soft fingertips capture contact geometry. Researchers have used this approach to estimate object shape and pose, but computing the forces that correlate most with manipulation capabilities remains a challenge. Modeling tactile shear — the forces that arise when an object slides or rotates against a sensor — is crucial for building robots that can grasp objects, use tools, and perform complex manipulation skills.
Explore HydroShear
The HydroShear code, simulation demos, and real-world evaluation videos are available on the project page.
Our solution, HydroShear , gives simulators the ability to accurately model tactile forces, enabling robots to learn dexterous, contact-rich manipulation policies entirely in simulation. These policies transfer seamlessly to the real world with no modification, achieving a 93 percent average success rate across four challenging tasks. Bridging the tactile reality gap
Simulators for robot locomotion have found success in real-world applications because physics engines model rigid body dynamics and proprioceptive sensing well. But subtle tactile forces and shear feedback are notoriously difficult to simulate accurately. This has made it nearly impossible for tactile sensors trained on simulators through reinforcement learning to succeed when deployed on real robots. Existing tactile simulators face a fundamental trade-off. Physics-based methods like finite-element methods accurately model contact forces but...
Excerpt shown — open the source for the full document.