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arxiv: 2605.02347 · v2 · pith:NPZFDOKUnew · submitted 2026-05-04 · 💻 cs.RO

ShapeGrasp: Simultaneous Visuo-Haptic Shape Completion and Grasping for Improved Robot Manipulation

Lukas Rustler , Matej Hoffmann This is my paper

Pith reviewed 2026-07-01 07:57 UTC · model grok-4.3

classification 💻 cs.RO
keywords shape completionvisuo-haptic fusionrobot graspingimplicit surfacestactile feedback3D reconstructioniterative planningphysics-based simulation
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The pith

Robots refine 3D object shapes using tactile data from grasps to raise success rates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

ShapeGrasp runs an iterative loop that starts from one RGB-D view, completes the shape with an implicit model, plans grasps in rigid-body simulation, executes the chosen grasp, and then folds the new tactile contacts plus gripper occupancy back into the model. When a grasp fails the system re-estimates pose and tries again with the updated shape. The authors report that this closed loop raises grasp success to 84 percent with a three-finger gripper and 91 percent with a two-finger gripper while also improving every 3D reconstruction metric they measured. A reader would care because the method shows how a robot can treat its own physical interactions as additional geometric measurements rather than depending on perfect initial vision alone.

Core claim

The central claim is that coupling implicit surface visuo-haptic shape completion with physics-based grasp planning produces an iterative pipeline in which each executed grasp supplies tactile surface contacts and gripper body space that are fused back into the shape model. This update yields measurably better 3D reconstructions and directly improves the quality of subsequent grasp candidates. The pipeline is shown to outperform baselines on two real robots and two gripper types, and it is presented as the first method demonstrated to update shape representations after real-world grasps.

What carries the argument

The iterative grasp-and-complete pipeline that fuses tactile surface contacts and gripper body space into an implicit surface visuo-haptic shape model.

If this is right

  • Fusing grasp-derived geometric constraints improves 3D shape reconstruction quality across all evaluation metrics.
  • Grasp success reaches 84 percent with a three-finger gripper and 91 percent with a two-finger gripper while outperforming baselines.
  • Grasp failures are handled by re-estimating object pose and re-planning with the refined shape model.
  • The approach works across different robots and grippers without requiring perfect initial visual data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the fusion step remains stable across object classes, the method could reduce the need for high-resolution initial sensors in cluttered scenes.
  • The closed-loop structure suggests that interaction data collected during routine manipulation could be reused to maintain or improve shape models over time.
  • Extending the same fusion step to include slip or force measurements might further tighten the link between updated shape and grasp stability.

Load-bearing premise

Tactile surface contacts and gripper body space obtained from real grasps can be fused accurately into the implicit surface model to produce a measurably better 3D representation that directly improves grasp planning.

What would settle it

A side-by-side trial in which adding the post-grasp tactile and occupancy data produces no gain in reconstruction metrics or grasp success rate compared with the initial visual estimate alone would falsify the claim.

Figures

Figures reproduced from arXiv: 2605.02347 by Lukas Rustler, Matej Hoffmann.

Figure 1
Figure 1. Figure 1: Schematic operation of ShapeGrasp. RGB-D information from a fixed camera is used to create a initial point cloud X (blue line; done only once) and later to track the pose of the object. X is inserted into a deep neural network (blue line; done only once) to compute a completed shape O0. The shape is inserted into the proposed grasp planner and the best feasible grasp is executed. After the grasp, tactile i… view at source ↗
Figure 2
Figure 2. Figure 2: Example of real grasp (left) with front (middle) and top (right) view view at source ↗
Figure 3
Figure 3. Figure 3: The table with robot, light and camera (top left); the robots used view at source ↗
Figure 4
Figure 4. Figure 4: Examples of front (left) and side (right) views of visual point clouds (green) with touches (highlighted in black if normals are facing towards current view at source ↗
Figure 5
Figure 5. Figure 5: Precision and Recall for Barrett Hand (top) and Robotiq 2F-85 view at source ↗
Figure 6
Figure 6. Figure 6: Shape completion metrics for Barrett Hand. The boxplots show the 25th to 75th percentile in bold, non outlier values with whiskers and outliers with view at source ↗
Figure 7
Figure 7. Figure 7: Shape completion metrics for Robotiq 2F-85 gripper. The boxplots show the 25th to 75th percentile in bold, non outlier values with whiskers and view at source ↗
read the original abstract

Humans grasp unfamiliar objects by combining an initial visual estimate with tactile and proprioceptive feedback during interaction. We present ShapeGrasp, a robotic implementation of this approach. The proposed method is an iterative grasp-and-complete pipeline that couples implicit surface visuo-haptic shape completion (creation of full 3D shape from partial information) with physics-based grasp planning. From a single RGB-D view, ShapeGrasp infers a complete shape (point cloud or triangular mesh), generates candidate grasps via rigid-body simulation, and executes the best feasible grasp. Each grasp attempt yields additional geometric constraints -- tactile surface contacts and space occupied by the gripper body -- which are fused to update the object shape. Failures trigger pose re-estimation and regrasping using the refined shape. We evaluate ShapeGrasp in the real world using two different robots and grippers. To the best of our knowledge, this is the first approach that updates shape representations following a real-world grasp. We achieved superior results over baselines for both grippers (grasp success rate of 84% with a three-finger gripper and 91% with a two-finger gripper), while improving the 3D shape reconstruction quality in all evaluation metrics used.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript presents ShapeGrasp, a method for simultaneous visuo-haptic shape completion and grasping. The approach involves an iterative pipeline: from a single RGB-D view, it performs implicit surface completion to infer the full 3D shape, uses physics-based simulation for grasp planning, executes the grasp on a robot, and upon failure fuses tactile surface contacts and gripper body occupancy to update the shape representation for re-planning. The authors claim this is the first method to update shape representations following a real-world grasp and report grasp success rates of 84% with a three-finger gripper and 91% with a two-finger gripper, superior to baselines, along with improvements in 3D shape reconstruction metrics.

Significance. If the reported improvements are substantiated with rigorous experimental validation, this work could contribute to the field of robotic manipulation by demonstrating a practical closed-loop system that refines object shape models using haptic feedback from grasps. The real-world evaluation across two different grippers is a positive aspect. The integration of implicit representations with physics simulation and real execution has potential for broader applications in handling unknown objects.

major comments (1)
  1. [Abstract] Abstract: The abstract claims quantitative improvements in grasp success rates (84% and 91%) and shape reconstruction quality over baselines, but provides no information on the number of trials conducted, the implementation details of the baseline methods, any statistical tests used to validate the results, or the precise algorithm for fusing the tactile and gripper data into the implicit model. This absence makes it impossible to assess whether the reported success rates support the central claim of improved manipulation through shape updating.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract claims quantitative improvements in grasp success rates (84% and 91%) and shape reconstruction quality over baselines, but provides no information on the number of trials conducted, the implementation details of the baseline methods, any statistical tests used to validate the results, or the precise algorithm for fusing the tactile and gripper data into the implicit model. This absence makes it impossible to assess whether the reported success rates support the central claim of improved manipulation through shape updating.

    Authors: We agree that the abstract is high-level and omits these specifics, which are instead provided in the body of the manuscript (experimental protocol and trial counts in Section 4, baseline descriptions in Section 3.3, fusion procedure in Section 3.2). Abstracts are constrained by length and typically focus on contributions and headline results rather than full methodology. The central claim is supported by the detailed experiments and real-world evaluation across two grippers described in the paper. To address the concern directly, we will revise the abstract to include a concise reference to the scale of the real-world trials and the iterative fusion of tactile contacts with gripper occupancy. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper describes an empirical iterative pipeline (RGB-D completion, physics-based grasp planning, real execution, post-grasp fusion of tactile contacts and gripper occupancy into an implicit visuo-haptic model) evaluated on two robots/grippers. Reported outcomes are direct experimental metrics (84%/91% grasp success, improved reconstruction scores) with no equations, loss functions, fitted parameters, or predictions that reduce to input definitions. No self-citation chains, uniqueness theorems, or ansatzes are invoked to support the central claim. The mechanism is a standard occupancy fusion step inside an implicit surface representation, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated.

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