Sensorless damage-safe grasping

Yusei Shuto; Danilo Vasconcellos Vargas

cs.ROAug 25, 2026

Sensorless damage-safe grasping

Authors: Yusei Shuto, Danilo Vasconcellos Vargas

Organizations: Kyushu University, Fukuoka, Japan

Abstract

Robotic fruit harvesting must hold produce securely without bruising it, yet compression stiffness varies several-fold with ripeness within a single species, so no fixed grip force spans the range. Rather than tune force, we bound deformation: a controller closes the gripper until the object's estimated compression strain reaches a user-specified limit $\varepsilon$ , using only the encoder position and motor-effort signal on every servo gripper---no tactile or force-torque sensor. Dividing an effort-based contact force by a lower bound on object stiffness makes the stop provably conservative---true compression stays at or below $\varepsilon$ ---for any $\varepsilon$ above a contact-detection strain floor we identify and quantify: robust detection itself spends compression, linearly in closing speed, making speed an explicit throughput--gentleness knob. Unlike a hand-tuned force threshold, $\varepsilon$ is a certified, size-scaling, operator-interpretable damage limit, and a ready safe-action parameter for learned grasping policies. In MuJoCo simulation over a realistic fruit-stiffness range, under a sensor-noise model calibrated to the real servo, the controller holds $\ge 98\,\%$ grasp at $0\,\%$ damage across all medium-to-firm stiffnesses for the entire certified $\varepsilon$ range, which neither fixed-force baseline attains; on stiffness-graded 3D-printed TPU cubes it matches baseline grasp success at roughly half the grip force and cuts soft-object damage from $100\,\%$ to $40\,\%$ .

Figures & tables

Panel of Figure 1 — Figure 1: Positioning of this work. Prior damage-aware grasping either couples dedicated tactile/vision sensing with heuristic (learned or hand-tuned) damage handling, or applies a fixed hand-tuned force with no damage model; sensorless force estimation supplies the signal but no damage bound. This work occupies the previously empty region: a certified deformation bound from proprioceptive signals alone.

Figure 2: Method pipeline. Encoder position $x$ and motor effort $I$ are read at $50\,\mathrm{Hz}$ ; the estimation block detects contact by amplitude threshold and computes the compression-strain estimate $\hat{r}=\hat{F}/(k_{\min}D)$ ; the control block applies a proportional law that drives $\hat{r}\to\varepsilon$ , producing the velocity command (close or hold). Fruit diameter $D$ and the user-set bound $\varepsilon$ enter as external inputs; the stiffness lower bound $k_{\min}$ sets the strain scale inside the estimate.

Figure 3: Conservative compression-strain bound (§ 3.5 ). The controller divides the current-based force by the stiffness lower bound $k_{\min}$ , so its estimate $\hat{r}$ over-bounds the true strain $r_{\mathrm{true}}$ (which scales with the actual $k_{\mathrm{true}}\geq k_{\min}$ ); stopping when $\hat{r}=\varepsilon$ leaves the true compression at $\varepsilon\,k_{\min}/k_{\mathrm{true}}\leq\varepsilon$ , the gap being the certified conservatism.

Figure 4: Simulation environment: the SO-ARM101 sandwiches a cube (fruit proxy, red) between two large jaw pads in MuJoCo. Cube side $D$ and whole-fruit stiffness $k$ are sampled per episode.

Method	$F$ at term.	Grasp	Damage
Proposed ( $\varepsilon=0.7\,\%$ )	$1.0\,\mathrm{N}$	100 %	0 %
Proposed ( $\varepsilon=1.3\,\%$ )	$1.0\,\mathrm{N}$	98 %	0 %
Proposed ( $\varepsilon=1.4\,\%$ )	$1.0\,\mathrm{N}$	100 %	0 %
Proposed ( $\varepsilon=1.5\,\%$ )	$1.2\,\mathrm{N}$	98 %	0 %
Proposed ( $\varepsilon=2.0\,\%$ )	$3.1\,\mathrm{N}$	100 %	0 %
Baseline aggressive ( $0.10\,\mathrm{A}$ )	$11.4\,\mathrm{N}$	100 %	100 %

Table 1: Headline comparison at

k=4000\,\mathrm{N/m}

(medium-soft tomato, 50 seeds per cell). Per-controller grasp success, damage rate (

r_{\text{hook}}>1.5\,\%

sustained for

\geq 5

ticks), and mean grip force

F

at closing exit.

Method	$k\,(\mathrm{N/m})$
Method	2000	4000	6000	8000	10 000
Proposed ( $\varepsilon=0.3\,\%$ )	00 6 / 00 0	0 64 / 00 0	0 62 / 00 0	0 52 / 00 0	0 38 / 00 0
Proposed ( $\varepsilon=0.7\,\%$ )	00 0 / 00 0	100 / 00 0	100 / 00 0	0 98 / 00 0	100 / 00 0
Proposed ( $\varepsilon=1.3\,\%$ )	0 34 / 00 0	0 98 / 00 0	100 / 00 0	100 / 00 0	100 / 00 0
Proposed ( $\varepsilon=1.4\,\%$ )	0 48 / 00 0	100 / 00 0	100 / 00 0	100 / 00 0	100 / 00 0
Proposed ( $\varepsilon=1.5\,\%$ )	0 40 / 00 6	0 98 / 00 0	100 / 00 0	100 / 00 0	100 / 00 0

Table 2: Sim grid: grasp / damage rate (%) across

(\varepsilon,k)

for the proposed method and the two fixed-force baselines (2000 trials; 50 seeds per cell;

D\in\{35,40,45\}\,\mathrm{mm}

pooled). Bold

=

clean corner (100 / 0); 17 of 30 proposed cells hit it.

Method	Cube	Grasp	Damage	Timeout	Mean $\hat{F}$
Proposed ( $\varepsilon=0.7\,\%$ )	soft	100 %	40 %	0 %	53
Proposed ( $\varepsilon=0.7\,\%$ )	medium	100 %	0 %	0 %	52
Proposed ( $\varepsilon=0.7\,\%$ )	stiff	80 %	0 %	0 %	49
Proposed ( $\varepsilon=1.3\,\%$ )	soft	100 %	80 %	0 %	55
Proposed ( $\varepsilon=1.3\,\%$ )	medium	100 %	0 %	0 %	56
Proposed ( $\varepsilon=1.3\,\%$ )	stiff	100 %	0 %	0 %	60

Table 3: Real-world validation on the SO-ARM101 with TPU cubes.

Figure 5: Representative real-world trial sequences on a soft TPU cube. Top: the proposed method at $\varepsilon=0.7\,\%$ — grasp succeeds without visible damage. Bottom: the fixed-high baseline ( $F_{\mathrm{th}}=25$ load units) — the controller times out and the cube is visibly damaged. Columns: pre-contact, grasp commit, lifted, final.

Figure 6: Measured mean true compression strain across stiffness (solid) against the equality-case prediction $\varepsilon\,k_{\min}/k_{\mathrm{true}}$ (dashed), with detection latency suppressed (reduced speed during the persistence window; simulation). The strain stays below the user bound $\varepsilon$ throughout and follows the prediction’s $1/k$ shape down to the residual ${\approx}0.15\,\%$ threshold term — visible as the $\varepsilon=0.7\,\%$ curve flattening. At the operating speed of the main sweep the latency term dominates instead (Fig. 7 ).

Figure 7: Settled strain at stop versus closing speed ( $k=10^{4}\,\mathrm{N/m}$ , $\varepsilon=0.7\,\%$ , 10 seeds per point, simulation). The detection floor is linear in $v$ with an intercept set by the noise-robust threshold (fit: $0.10\,\%+0.093\,\%\cdot v\,[\mathrm{mm/s}]$ ), matching $\varepsilon_{\mathrm{floor}}$ of § 6.3 : closing speed is the operator’s throughput–gentleness knob. All points remain below the user bound $\varepsilon$ .

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Robots are increasingly used in unstructured environments. The need for them to safely grasp unknown objects without damaging them becomes crucial. Humans achieve this by sensing and quickly responding by adjusting their grasping force. Similarly, effective grasp acquisition in robots requires compliant interaction strategies that can adapt to uncertain object properties and adjust to any instabilities during manipulation. We present a geometry-aware force/torque-based contact estimation method for a coarse position-controlled gripper, combined with an adaptive admittance controller for safe grasp acquisition. The desired contact forces are estimated online to keep stable contact with objects of unknown properties. This enables compliant and stable grasps while avoiding excessive forces. Experiments with objects of different sizes, shapes, stiffnesses, and weights show that the proposed algorithm not only prevents slippage but also applies minimal force to safely grasp an object without causing excessive deformation.

Abstract

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