Notes

Lab Notes

Short, technical notes from the lab — the kind of things that usually only live in git commit messages and paper footnotes. Written for engineers and students who build interactive simulations.

· ~5 min read

Why haptic rendering needs a 1 kHz update loop

The eye forgives a dropped frame; the hand does not. The visual loop of a VR simulator runs at 90 Hz, but haptic force feedback has to be recomputed at roughly 1 kHz. If forces arrive too slowly, the feedback becomes unstable: the haptic device starts to vibrate or "buzz", and the virtual surface feels soft and mushy instead of hard.

This separation of rates shaped our material cutting model: a fast haptic thread computes collision and force for the tool tip, while the actual removal of virtual tissue happens in a coarser simulation step. The geometry handed to the haptic thread is updated asynchronously, which keeps the 1 kHz budget intact. The trick is keeping the two representations consistent enough that the user never notices the lag between what they cut and what they feel.

We showed that this model runs at interactive rates on consumer VR hardware — see “A Continuous Material Cutting Model with Haptic Feedback for Medical Simulations” (IEEE VR, 2019).

haptics real-time material removal
· ~6 min read

Cutting a tooth with three layers

A real tooth is not a solid object, and a simulator that treats it as one feels wrong the moment the drill enters the dentin. We model every tooth with three tissue layers — enamel, dentin, and pulp — each with its own hardness. The drill must cut continuously through these layers and the user must feel the material change, otherwise the haptic sensation collapses into "drilling through cheese".

The second lesson from the dental simulator is that depth perception is a combination of stereopsis and hand alignment. With true stereoscopic 3D in the headset and the physical hand aligned with the virtual tool, students made fewer errors — and those skills transferred to real phantom heads. In the randomized controlled trial with 83 dental students, simulator training significantly improved real-world clinical skills.

The follow-up detail that surprised us most: the mirror. Students learn to work under indirect vision, and guiding them through the mirror — visually, verbally, or haptically — changes how quickly they learn. See “Reflecting on Excellence” (IEEE VR, 2024) and the PLOS ONE study on stereopsis and hand-tool alignment.

dentistry stereopsis RCT
· ~4 min read

Inner sphere trees: high forces without the buzz

Hip reaming is the hard case for haptic rendering: forces up to 137 N, extremely stiff contacts, and two hands in the loop. Most collision detection structures for haptics are built for light touch — they fail here, because a single stiff contact can send the force computation into oscillation.

The solution developed in the HIPS project is a data structure based on sphere packings: the bone volume is decomposed into overlapping spheres that are trivial to query for penetration. The structure stays stable under extreme stiffness, which is why the simulator could be validated with real surgeon tools mounted on a KUKA LBR iiwa robot — and why over a dozen surgeons confirmed the realism of the haptic sensation.

Full background in the Inner Sphere Trees project page and the HIPS TVCG paper (2025).

collision detection haptics high forces
· ~3 min read

Why we test simulators with real surgeons

A simulator can look perfect in a demo and still fail in the clinic. That is why every major result from my projects was validated with domain experts: over a dozen surgeons evaluated the haptic sensation of HIPS and nearly all of them recommended it for training students and residents; two independent expert dentists scored the drilling outcomes of the 40 trained students in the dental study.

The pattern that works for us: build the physics first, then validate the perception with experts, then measure learning transfer with a controlled study. In that order — each step catches errors the previous one cannot see.

evaluation user studies method