FORM: Robot Manipulation through Direct Material Law Identification

Stepan Tretiakov; Ruihan Zhao; Cheng-Hsi Hsiao; Xingjian Li; Adam Thorpe; Hassan Iqbal; Sandeep Chinchali; Ufuk Topcu

cs.ROSep 29, 2026

FORM: Robot Manipulation through Direct Material Law Identification

Authors: Stepan Tretiakov, Ruihan Zhao, Cheng-Hsi Hsiao, Xingjian Li, Adam Thorpe, Hassan Iqbal, Sandeep Chinchali, Ufuk Topcu, +1 more

Organizations: Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA. · Chandra Family Department of Electrical and Computer Engineering · Maseeh Department of Civil, Architectural and Environmental Engineering · Oden Institute for Computational Engineering and Sciences · Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA.

Abstract

When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online identification is therefore essential for reliable manipulation. We present FORM (From Observed Response to Material laws), which identifies material properties from a single robot interaction and reuses the recovered model to plan manipulation under new actions and geometries. We use weak-form momentum balance to convert observed material motion and contact forces into linear equations in the unknown material parameters. These equations are assembled using the same material point method discretization as the forward simulator, so identification reduces to linear least-squares solves whose solutions can be used directly for prediction without refitting or conversion. Across four material classes, FORM reduces identification time from roughly 10--25 minutes for iterative baselines to 2--5 seconds, while maintaining competitive accuracy on new motions, initial conditions, and geometries. We demonstrate our approach in simulation and on hardware across four manipulation tasks: elastic rod insertion, golf putting with an elastic club, elastoplastic shaping, and target-volume pouring. In each task, the model identified from a single interaction is reused to plan new motions or manipulate a different geometry. FORM estimates elastic properties within 3.4% and elastoplastic properties within 2%, achieves 72.4--77.8% IoU in dough shaping, and keeps mean pouring error at 3.8 mL across target volumes of 60--160 mL.

Figures & tables

Fig. 1: The dynamics of deformable materials and fluids are often hard to observe from vision alone. Active physical interaction is required to estimate their latent properties and enable robust manipulation policies. (Top) Starting from the same initial shape, applying the identical robot action such as a 25 mm pinch or a 10 N press results in different deformations dictated by the stiffness and yield behavior. (Bottom) Similarly, applying the same pouring action at 10 degrees per second results in distinct flow behaviors governed by the varying viscosities of the fluids.

Fig. 2: FORM: material identification and manipulation. (a) Synthetic stereo tracks and motion reconstructed from real RGB-D observations. Colors indicate image displacement in stereo and initial height in RGB-D. The force trace is from the synthetic press. (b) Divergence-free test fields remove pressure. Green arrows denote test vectors, not motion. The weak balance identifies stiffness $E$ and yield threshold $Y$ . The stress–strain curve is schematic. (c) The recovered model and target are used to plan the robot motion in MPM, illustrated here in the simulated shaping task.

Law	Stress response	Parameters
Elastic and hyperelastic , with $p=-\kappa(J-1)$ for the $\tau$ rows
neo-Hookean	$\displaystyle\tau=\frac{2C_{10}}{J}\,\mathrm{dev}\,\bar{b}$	$C_{10},\kappa$
Mooney–Rivlin	$\displaystyle\tau=\frac{2}{J}\,\mathrm{dev}\!\left[C_{10}\bar{b}+C_{01}(\bar{I}_{1}\bar{b}-\bar{b}^{2})\right]$	$C_{10},C_{01},\kappa$
Yeoh	$\displaystyle\tau=\frac{2}{J}\Big[\sum_{i=1}^{3}iC_{i}(\bar{I}_{1}-3)^{i-1}\Big]\mathrm{dev}\,\bar{b}$	$C_{1},C_{2},C_{3},\kappa$
Gent	$\displaystyle\tau=\frac{\mu}{J\big[1-(\bar{I}_{1}-3)/J_{m}\big]}\,\mathrm{dev}\,\bar{b}$	$\mu,J_{m},\kappa$
fixed-corotated	$\displaystyle\sigma=\frac{2\mu}{J}(F-R)F^{\mathsf{T}}+\lambda(J-1)I$	$\mu,\lambda$

TABLE I: Constitutive models and parameters, with

\sigma=-pI+\tau

. Stresses are Cauchy unless marked

\mathrm{K}

for Kirchhoff.

	Jell-O		Sand		Plasticine		Water
Method	Mean Loss	Solve Time (s)	Mean Loss	Solve Time (s)	Mean Loss	Solve Time (s)	Mean Loss	Solve Time (s)
NCLaw	5.2e-4	1513.2	1.6e-4	1305.6	1.2e-4	1300.2	5.7e-4	1306.8
Diff. Sys-Id	1.2e-8	715.8	6.3e-13	1132.8	1.6e-10	1028.4	4.0e-9	556.2
Ours (Fn Enc.)	3.5e-4	5.2	3.3e-8	0.1	2.9e-6	5.3	2.3e-6	4.8
Ours (Full)	5.1e-6	4.4	1.4e-8	2.4	5.2e-7	4.7	2.3e-6	2.2
Ours (No stress)	5.1e-6	3.5	2.0e-8	6.0	5.2e-7	6.5	2.3e-6	1.6

TABLE II: Reconstruction results for FORM and baselines. Variants use learned bases selected by material type ( Ours (Fn Enc.) ), full information ( Ours (Full) ), observations without stress ( Ours (No stress) ), or position-only observations ( Ours (Pos. only) ). Bold marks a lowest-loss non-oracle result and its solve time.

Fig. 3: Stiffness from a common bending probe transfers to rod insertion (top) and elastic-club putting (bottom). Blue and orange denote materials A and B. The pale outline marks the initial rod pose. Putting shows B and A at the end of the backswing, then A at contact and follow-through. Arcs measure projected grip-to-head inclination relative to horizontal; white balls and timestamps show recorded motion states. Colored balls overlay final positions from four separate executions, with color denoting club material. The circle marks the stopping target.

Fig. 4: Material identification and X shaping in simulation. Left: A’s identification press and final planned pinch. The arrow indicates plate motion; translucent cylinders show the simulated fingers, with the hand illustrating their pose. Right: target shape and matched and swapped results for A (blue) and B (orange), all at a common scale. Results are shown $1$ s after finger withdrawal; dashed outlines mark the target.

Fig. 5: Hardware pressing and shaping of red Play-Doh, yellow butter slime, and gray plasticine (left to right). Top: recorded pressing. Middle: force-driven MPM predictions at matching times, using models identified from separate presses. Simulation views are horizontally mirrored for display. Bottom: reconstructed shapes after four open-loop pinches, rigidly aligned to the dashed target outlines without scaling.

Fig. 6: Glycerol pouring with an identified model. (a) Hardware and MPM at matching times during the $60^{\circ}$ identification pour; this recording supplies both viscosity identification and effective contact calibration. Simulated liquid is colored blue for visibility. (b) Glycerol: frozen MPM predictions and hardware mean $\pm$ sample standard deviation over five trials per target. Water: one hardware trial per target, without error bars. (c) Repeat 2: target $\rightarrow$ measured receiver volume (mL), with readings rounded to the nearest mL.

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Accurate physical parameter identification of manipulated objects is fundamental to advanced robotic manipulation and the construction of faithful digital twins. However, acquiring physically consistent inertial and frictional properties from real-world interactions remains challenging due to sensing noise, modeling errors, and limited prior knowledge. This paper presents RigPI, a systematic framework for identifying dynamic parameters of both unconstrained rigid bodies and multi-link rigid bodies during robot-object interaction. RigPI integrates vision-based semantic priors, force-torque measurements, and motion observations within a differentiable simulation pipeline. A vision-language model (VLM) provides informed initialization and a constrained search space, while gradient information from a differentiable physics simulator enables efficient and stable parameter refinement. The proposed two-stage optimization strategy alleviates sensitivity to noise and avoids physically implausible solutions. Extensive real-world experiments on objects with revolute and prismatic joints demonstrate that RigPI achieves accurate and stable parameter estimates, and successfully reproduces manipulation trajectories on a real robot with parameter-aware predictive validity. These results highlight the effectiveness and robustness of RigPI for real-world robotic system identification tasks.

Abstract

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