Amazon and University of Michigan give robots a sense of touch

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. 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 are too slow for training reinforcement learning policies at scale. Faster approximations, on the other hand, oversimplify how forces build up and change during contact. They miss critical events like the moment a gripped object begins to slide or the way a soft sensor deforms over time. Modeling touch with fidelity and speed HydroShear’s key innovation is to add new capabilities to an existing physics simulation technique known as hydroelastic contact models. Called path-dependent force tracking, this approach accurately tracks how forces accumulate over a soft sensor membrane during a physical interaction. Rather than computing forces based only on instantaneous contact, HydroShear remembers the motion history of the object as it moves across the sensor. More concretely, when a robot grasps an object and moves it, different points on the object’s surface come into contact with the sensor at different times. HydroShear tracks each of these contact points individually, computing how the soft elastomer deforms as the object moves. It then converts these deformations into realistic force fields, accounting for friction, slipping, and the elastomer’s material properties. The simulator handles full 3-D motion — tilting and rolling as well as in-plane sliding — which is essential for dexterous manipulation. It’s also GPU parallelizable, enabling efficient large-scale policy training. We calibrate HydroShear by collecting controlled real-world data with a robot arm and vision-based GelSight Mini tactile sensors. The calibration isolates four key parameters: how forces dissipate across the sensor surface, how tangential and normal forces build up, and the friction coefficient between the object and the elastomer. This systematic approach ensures that the simulator accurately reproduces real tactile feedback. Validation: From simulation to real robots We evaluated HydroShear on four contact-rich manipulation tasks, each highlighting different challenges. In all tasks, the robot perceives touch and proprioception (joint positions and gripper state) and has no access to object poses. Peg insertion: The robot grasps a cylindrical peg at an unknown orientation and must insert it into a tight socket. Because the grasp pose varies with each trial, the robot must use tactile feedback alone to detect and correct alignment errors during insertion. Bin packing: The robot inserts a cube into a target slot within a crowded bin. Neighboring cubes partially block the slot, so the robot must push through multiobject contact while sensing forces from multiple directions simultaneously. Book shelving: The robot inserts a book laterally into a shelf, with gravity pulling perpendicular to the insertion direction. The book is larger than the fingertip, producing broad contact patches that make it difficult to localize the object from touch alone. Drawer pulling: The robot pulls open a drawer while external-force perturbations are applied at random times. The robot must detect when the handle begins to slip and tighten its grip just enough to maintain hold without crushing it. We trained reinforcement learning policies entirely in simulation using HydroShear, then deployed them on a real Franka robot with GelSight Mini sensors without any modification or fine tuning. HydroShear achieved a 93% average success rate across all four tasks. We compared against two strong baselines: TacSL, which uses simplified force approximations, and FOTS, a recent learning-based method. TacSL achieved only 34% success, while FOTS reached 58 to 61%. The performance gap underscores the importance of accurate tactile shear simulation. Interestingly, the performance difference correlates directly with simulation fidelity. On tasks like peg insertion, where precise force feedback is critical, HydroShear’s advantage is most pronounced. On drawer pulling, which requires detecting and reacting to slippage, HydroShear’s path-dependent force tracking proves essential. Faster and less expensive Accurate tactile simulation unlocks a powerful recipe for robot learning: train policies entirely in simulation, then deploy them on real robots. This approach is dramatically faster and cheaper than learning from real-world interactions, which can damage sensors and require extensive trial and error. For warehouse automation, the approach is particularly valuable. Tasks like bin packing, sorting, and careful handling require robots to feel their way through complex interactions. HydroShear enables robots to learn such skills without extensive real-world data collection. While HydroShear yields strong results when coupled with vision-based tactile sensors like GelSight, the underlying principles could extend to other tactile modalities. We’re also exploring how higher-resolution sensor simulations and more-complex object geometries could further improve performance.