Amazon and University of Michigan give robots a sense of touch

2026-07-10 · Source: Amazon Science homepage · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Engineering & Applied Sciences · Depth: Advanced, long

Summary

Amazon and the University of Michigan have developed HydroShear, a new physics-based simulator designed to teach robots complex manipulation tasks using their sense of touch. Released on July 10, 2026, HydroShear introduces path-dependent force tracking into hydroelastic contact models, accurately simulating tactile shear forces that are crucial for dexterous manipulation. This innovation allows robots to learn contact-rich policies entirely in simulation, which then transfer seamlessly to real-world applications. Evaluated on a Franka robot equipped with GelSight Mini sensors, HydroShear achieved an impressive 93% average success rate across four challenging tasks: peg insertion, bin packing, book shelving, and drawer pulling. This significantly surpasses existing baselines like TacSL (34%) and FOTS (58-61%), demonstrating its superior fidelity and speed for large-scale policy training.

Key takeaway

For Robotics Engineers developing tactile manipulation systems, HydroShear offers a significant advancement in sim-to-real transfer. You should consider integrating high-fidelity, path-dependent tactile simulation into your training pipelines to drastically reduce real-world data collection and accelerate policy development. This approach allows you to achieve robust performance on complex, contact-rich tasks like peg insertion and bin packing more efficiently and cost-effectively.

Key insights

HydroShear's path-dependent force tracking in hydroelastic contact models enables robots to learn dexterous manipulation in simulation with high real-world transferability.

Principles

• Accurate tactile shear simulation is critical for robot dexterity.
• Sim-to-real transfer requires high-fidelity force modeling.
• Path-dependent force tracking improves simulation realism.

Method

HydroShear extends hydroelastic contact models with path-dependent force tracking, remembering object motion history to compute realistic force fields, accounting for friction, slipping, and material deformation. It is calibrated with real-world data.

In practice

• Train robot manipulation policies entirely in simulation.
• Reduce real-world data collection for tactile tasks.
• Apply to warehouse automation tasks like bin packing.

Topics

• HydroShear
• Robotic Manipulation
• Tactile Sensing
• Simulation
• Reinforcement Learning
• Sim-to-Real Transfer

Best for: Research Scientist, Robotics Engineer, AI Scientist, Machine Learning Engineer

Related on AIssential

• Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning — Calibrated simulation and geometry-aware tactile representations enable blind dexterous grasping on physical robots.
• ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation — CCGE leverages state-conditioned contact coverage to enable efficient, general-purpose exploration in dexterous manipulation.
• Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation — Center-of-Pressure (CoP) enables robust sim-to-real dexterous manipulation by preserving dense, physics-grounded tactile information.
• TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments — Tactile sensing, structured learning, and real-hardware training enable robots to identify objects without vision.
• Inference-time Policy Steering via Vision and Touch — ViTaL integrates vision and touch for robust robot policy steering in contact-rich manipulation.
• Dense Force Estimation with an Event-based Optical Tactile Sensor — Event-based tactile sensors can reconstruct dense 3D force fields by mapping displacement data to forces via iFEM and neural networks.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Amazon Science homepage.