ForceTwin: Physics-informed Digital Twins for Robotic Manipulation
ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction
For robotics researchers, ForceTwin offers a more accurate way to model object physics, which is crucial for improving robotic manipulation tasks.
A new system called ForceTwin creates digital twins of objects by having a person interact with them using a force-sensing gripper.
It estimates physical properties like inertia and friction, which existing methods often miss.In experiments, ForceTwin improved robotic manipulation success rates from 60% to 87% on various objects compared to other methods.
ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction
Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these include inertia, friction, and mechanisms such as springs or door closers, whose effects can vary with configuration and velocity. Such properties are not directly observable from appearance: visually identical doors may require very different effort to manipulate. Existing digital-twin pipelines recover primarily kinematics or assign static physical parameters from visual and language priors, which can yield physically implausible estimates. As a result, state-dependent mechanism dynamics remain unidentified and are not represented in standard asset formats. We present ForceTwin, a system for identifying physics-informed digital twins of articulated objects from instrumented human interaction. A person probes an object using a handheld force-sensing gripper, providing synchronized poses and interaction forces from which we estimate the articulation, parametric dynamics including inertia, Coulomb friction, viscous damping, and a structured neural residual capturing state-dependent mechanism forces. ForceTwin nearly halves the inertial-parameter error of a VLM prior. As a feedforward dynamics model for impedance control on a Spot and a Franka FR3, ForceTwin achieves 87% goal completion across nine object-embodiment pairs, compared with 60% using VLM-prior and 57% using kinematics-only twins, with the largest gains on objects whose strong mechanisms cause both baselines to stall. We further use the identified twins to train whole-body door-traversal policies and deploy them in the real world. Project Page: https://timengelbracht.github.io/forcetwin-website/
This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include digital twins, robotic manipulation, physics-informed models, force sensing.
Source attribution: curated from arXiv cs.AI (@Tim Engelbracht, René Zurbrügg, Mayank Mittal, Marco Hutter, Marc Pollefeys, Hermann Blum, Zuria Bauer).
