AI and Robotic Massage, Delivered On-Demand

At Aescape’s New York headquarters, the hum of robotics is tuned not for assembly lines but for relaxation. The company, founded by entrepreneur Eric Litman, has built an automated massage system that combines artificial intelligence, sensing, and robotics to deliver personalized treatments—no human hands required.

“Working at the intersection of the human body, AI, and robotics means we have an opportunity to create something very unique,” Litman said. “We’re building a system capable of deeply understanding us and engaging with us in ways that go even beyond what therapists can do today.”

Every session begins with a scan. Depth sensors mounted overhead generate over 1.1 million data points across the user’s body, constructing a 3D model that informs the robot’s treatment plan. The system then uses machine learning algorithms and biomechanical mapping to guide robotic arms with millimeter-level precision.

Each arm is force-sensitive, allowing it to adjust pressure dynamically in real time, responding to body curvature, tissue stiffness, and user feedback. That capability depends on a tight feedback loop between sensors and motion controls, translating subtle mechanical resistance into precise actuation.

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“The arms themselves are force-sensitive, and that’s a relatively new field of robotics,” Litman explained. “It required a tremendous amount of work figuring out how to move the arm in a way that felt familiar and conveyed humanity in the process.”

To replicate the feel of a human massage, Aescape’s team studied the gestures of professional therapists, outfitting them with hand sensors to capture motion and pressure. Contrary to what one might expect, fingers weren’t the primary tools. Instead, the company modeled its AirPoints—the robot’s end-effectors—after the palm, forearm, and elbow. Software control enables these surfaces to deliver pressure from any direction, reproducing both broad, sweeping motions and deep, localized techniques.

Aescape’s AI controls the mechanics and learns from them. During and after each session, user feedback such as preferred pressure, duration, and target areas is anonymized and added to a collective learning model. Over time, this data refines how the system personalizes each massage and adapts to new body types and use cases.

“All of those signals eventually help to train the system to get smarter about you,” Litman said. “The collective of all the people getting massages helps it learn how to deliver really good care across all of them.”

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The company’s mobile app acts as the control hub, allowing users to choose treatments, adjust intensity in real time, and even modify ambient lighting and music. Beneath that seamless interface lies a network of depth sensors, motion planning algorithms, and high-speed control software that synchronizes the movement of dual robotic arms with the user’s exact body position.

What began as Litman’s personal search for relief from a bulging cervical disc has evolved into a scalable system for automated care. Early skepticism from investors only pushed him further to prove that precision robotics could deliver both performance and comfort.

“Hearing that this was a really hard problem just drove me to want to do it even more,” he said. “We had to leverage our optimism and break through every hurdle that existed to bring something this complex to market.”

As the technology matures, Aescape is expanding its capabilities in both hardware and software, aiming to make robotic massage as accessible as booking a rideshare.

“Five years from now, we see a world where massage therapy becomes much more accessible,” Litman said. “We’re listening to feedback from our partners and members, and all of that is going to inform a really exciting set of new capabilities.”

Video by ASME’s Video Production Team. Article by Aida M. Toro.