pith. sign in

arxiv: 1907.04761 · v1 · pith:VDEDGFNGnew · submitted 2019-07-10 · 💻 cs.RO · cs.CV· cs.LG

Towards Affordance Prediction with Vision via Task Oriented Grasp Quality Metrics

Luca Cavalli , Gianpaolo Di Pietro , Matteo Matteucci This is my paper

Pith reviewed 2026-05-24 23:50 UTC · model grok-4.3

classification 💻 cs.RO cs.CVcs.LG
keywords task-oriented graspinggrasp quality metricsaffordance functionsvision-based predictionrobotic graspingrange imagesdeep learning for manipulation
0
0 comments X

The pith

Task-oriented grasp quality can be quantified by defining affordance functions from basic grasp metrics and inferred from vision.

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

The paper sets out to extend standard grasp quality measures so they account for the specific task a grasp will serve. It does this by constructing affordance functions that combine existing basic metrics into task-specific scores for any number of tasks. The authors then test whether these scores can be computed exactly from full object models and whether deep models can learn to predict the same scores from partial visual data such as range images. A reader would care because the approach aims to let robots choose grasps suited to intended actions rather than generic stability alone.

Core claim

The central claim is that the concept of grasp quality metric extends to task-oriented grasping by defining affordance functions via basic grasp metrics for an open set of task affordances, and that these functions can be evaluated both with known object models in simulation and by training deep models to infer them from synthesized range images.

What carries the argument

Affordance functions constructed by combining basic grasp quality metrics into task-specific scores.

If this is right

  • Physical metrics of grasp hypotheses can be defined and computed directly in known-object simulation.
  • Deep models can be trained to predict the same task-oriented values from range images alone.
  • The same construction applies across an open set of task affordances without requiring new metric definitions for each one.
  • Validity can be checked separately in perfect-information and partial-information regimes.

Where Pith is reading between the lines

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

  • A robot could select among grasp candidates by comparing their predicted affordance scores for a commanded task using only camera data.
  • The combination approach might be tested on tasks that involve sequences of actions rather than single grasps.
  • Partial-information accuracy could be improved by adding multi-view or RGB inputs to the range-image training.
  • The framework leaves open whether the same affordance functions transfer across different robot hands or object materials.

Load-bearing premise

Basic grasp metrics can be combined into task-specific affordance functions whose values remain useful when inferred from partial visual information.

What would settle it

A physical experiment in which the vision-inferred affordance values fail to predict measured task success rates for the same grasps would falsify the framework's practical utility.

Figures

Figures reproduced from arXiv: 1907.04761 by Gianpaolo Di Pietro, Luca Cavalli, Matteo Matteucci.

Figure 1
Figure 1. Figure 1: Best grasp, according to the proposed metrics, relatively to the ˜ [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The three phases of our grasp policy: (a) the pregrasp parameters determine the initial position and posture of the hand (b) the hand approaches in a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pregrasp degree of freedom (a) set to 0, (b) set to 0.25, (c) set to 1 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Optimized grasps from the picking showing the joint pinch strategy. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Optimized grasps from the cutting task showing two different cutting [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Affordance function parameter variability. The grasp strategy varies [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: We formally consider point clouds equivalent to [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 9
Figure 9. Figure 9: Synthesized range images with camera-in-hand perspective. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Global minimum score cumulative distributions for the three [PITH_FULL_IMAGE:figures/full_fig_p007_11.png] view at source ↗
read the original abstract

While many quality metrics exist to evaluate the quality of a grasp by itself, no clear quantification of the quality of a grasp relatively to the task the grasp is used for has been defined yet. In this paper we propose a framework to extend the concept of grasp quality metric to task-oriented grasping by defining affordance functions via basic grasp metrics for an open set of task affordances. We evaluate both the effectivity of the proposed task oriented metrics and their practical applicability by learning to infer them from vision. Indeed, we assess the validity of our novel framework both in the context of perfect information, i.e., known object model, and in the partial information context, i.e., inferring task oriented metrics from vision, underlining advantages and limitations of both situations. In the former, physical metrics of grasp hypotheses on an object are defined and computed in known object model simulation, in the latter deep models are trained to infer such properties from partial information in the form of synthesized range images.

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

2 major / 2 minor

Summary. The paper proposes a framework to extend grasp quality metrics to task-oriented grasping by defining affordance functions from basic grasp metrics for an open set of task affordances. It evaluates the effectivity of these task-oriented metrics and their applicability by learning to infer them from vision, assessing validity both in perfect-information simulation with known object models (computing physical metrics on grasp hypotheses) and in partial-information settings (training deep models on synthesized range images).

Significance. If the central claim holds, the work would provide a principled way to quantify task-specific grasp quality and enable vision-based prediction of affordances, which could improve robotic manipulation for diverse tasks beyond generic grasp success. The dual evaluation contexts (simulation and learned inference) are a strength for assessing practical applicability.

major comments (2)
  1. [Abstract and evaluation sections] The load-bearing step is the definition and combination rule for affordance functions from basic grasp metrics (e.g., force-closure); the manuscript provides no independent validation (such as correlation with physical task success rates) that these functions remain predictive of task outcomes after regression from partial visual information, as opposed to merely matching simulation labels.
  2. [Abstract] No equations, explicit definitions of the affordance functions, or quantitative results (error metrics, success rates, or ablation studies) appear in the abstract or summary description, preventing verification that the framework is not an arbitrary aggregation.
minor comments (2)
  1. [Framework description] Clarify the exact set of basic grasp metrics used and how they are aggregated into each affordance function.
  2. [Vision inference experiments] Provide details on the deep model architecture, training data synthesis, and loss functions used for vision-based inference.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on the framework and evaluation. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract and evaluation sections] The load-bearing step is the definition and combination rule for affordance functions from basic grasp metrics (e.g., force-closure); the manuscript provides no independent validation (such as correlation with physical task success rates) that these functions remain predictive of task outcomes after regression from partial visual information, as opposed to merely matching simulation labels.

    Authors: We agree that the vision-based inference is trained and evaluated against simulation-computed labels rather than independent real-world task success rates. The known-model simulation provides the ground-truth task-oriented metrics via the defined affordance functions, and the range-image models are assessed on their ability to regress those labels. No physical robot experiments correlating predictions to actual task outcomes (e.g., pouring success) are reported. We will add an explicit limitations paragraph clarifying this scope and the distinction between simulation validation and real-task correlation. revision: partial

  2. Referee: [Abstract] No equations, explicit definitions of the affordance functions, or quantitative results (error metrics, success rates, or ablation studies) appear in the abstract or summary description, preventing verification that the framework is not an arbitrary aggregation.

    Authors: We accept this criticism. The submitted abstract is intentionally high-level and omits the mathematical definitions of the affordance functions as well as any numerical results. In the revision we will expand the abstract to include the core equations for combining basic grasp metrics into task-specific affordances together with summary quantitative metrics (e.g., mean absolute error on simulated test sets and grasp success rates under the learned predictors). revision: yes

standing simulated objections not resolved
  • Independent correlation of the learned affordance predictions with physical task success rates measured on a real robot, as opposed to matching simulation-derived labels.

Circularity Check

0 steps flagged

No circularity: affordance definitions and vision inference remain independent of fitted inputs

full rationale

The paper defines task-oriented affordance functions from basic grasp metrics and evaluates both simulation-based computation under perfect information and regression from range images. No equations, combination rules, or self-citations are supplied that would reduce the affordance values or their predictive ordering to the inputs by construction. The central mapping from grasp metrics to affordances is presented as a definitional extension rather than a fitted or self-referential step, and the vision-inference component is assessed separately via training on synthesized data. This leaves the derivation self-contained against external simulation benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.0 · 5707 in / 1039 out tokens · 16007 ms · 2026-05-24T23:50:34.751648+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · 1 internal anchor

  1. [1]

    Tactile experience-based robotic grasping,

    H. Dang and P. K. Allen, “Tactile experience-based robotic grasping,” in Workshop on Advances in Tactile Sensing and Touch based Human- Robot Interaction, HRI , 2012

    work page 2012

  2. [2]

    Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection,

    S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection,” The International Journal of Robotics Research , vol. 37, no. 4-5, pp. 421–436, 2018

    work page 2018

  3. [3]

    Eye-in-hand stereo visual servo- ing of an assistive robot arm in unstructured environments,

    D.-J. Kim, R. Lovelett, and A. Behal, “Eye-in-hand stereo visual servo- ing of an assistive robot arm in unstructured environments,” in Robotics and Automation, 2009. ICRA’09. IEEE International Conference on . IEEE, 2009, pp. 2326–2331

    work page 2009

  4. [4]

    Learning probabilistic discriminative models of grasp affordances under limited supervision,

    A. N. Erkan, O. Kroemer, R. Detry, Y . Altun, J. Piater, and J. Peters, “Learning probabilistic discriminative models of grasp affordances under limited supervision,” in Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on . IEEE, 2010, pp. 1586–1591

    work page 2010

  5. [5]

    Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching,

    A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo et al. , “Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching,” in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 1–8

    work page 2018

  6. [6]

    Grasp quality measures: review and perfor- mance,

    M. A. Roa and R. Su ´arez, “Grasp quality measures: review and perfor- mance,” Autonomous robots, vol. 38, no. 1, pp. 65–88, 2015

    work page 2015

  7. [7]

    Examples of 3d grasp quality computa- tions,

    A. T. Miller and P. K. Allen, “Examples of 3d grasp quality computa- tions,” in Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C) , vol. 2. IEEE, 1999, pp. 1240–1246

    work page 1999

  8. [8]

    Mcb-industrial robot feature article-barrett hand grasper,

    W. Townsend, “Mcb-industrial robot feature article-barrett hand grasper,” Industrial Robot: An International Journal , vol. 27, no. 3, pp. 181–188, 2000

    work page 2000

  9. [9]

    Graspit! a versatile simulator for robotic grasping,

    A. T. Miller and P. K. Allen, “Graspit! a versatile simulator for robotic grasping,” IEEE Robotics Automation Magazine, vol. 11, no. 4, pp. 110– 122, Dec 2004

    work page 2004

  10. [10]

    The princeton shape benchmark,

    P. Shilane, P. Min, M. Kazhdan, and T. Funkhouser, “The princeton shape benchmark,” in Proceedings Shape Modeling Applications, 2004. IEEE, 2004, pp. 167–178

    work page 2004

  11. [11]

    Gibson, The senses considered as perceptual systems

    J. Gibson, The senses considered as perceptual systems . Boston: Houghton Mifflin, 1966

    work page 1966

  12. [12]

    Affordances: Four points of debate,

    C. Michaels, “Affordances: Four points of debate,” ECOLOGICAL PSYCHOLOGY, vol. 15, pp. 135–148, 04 2003

    work page 2003

  13. [13]

    To afford or not to afford: A new formalization of affordances toward affordance- based robot control,

    E. S ¸ahin, M. C ¸ akmak, M. R. Do˘gar, E. U˘gur, and G. ¨Uc ¸oluk, “To afford or not to afford: A new formalization of affordances toward affordance- based robot control,” Adaptive Behavior , vol. 15, no. 4, pp. 447–472, 2007

    work page 2007

  14. [14]

    Task-oriented grasping using hand preshapes and task frames,

    M. Prats, P. J. Sanz, and A. P. Del Pobil, “Task-oriented grasping using hand preshapes and task frames,” in Robotics and Automation, 2007 IEEE International Conference on . IEEE, 2007, pp. 1794–1799

    work page 2007

  15. [15]

    Learning task con- straints for robot grasping using graphical models,

    D. Song, K. Huebner, V . Kyrki, and D. Kragic, “Learning task con- straints for robot grasping using graphical models,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2010, pp. 1579–1585

    work page 2010

  16. [16]

    Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task,

    H. Dang and P. K. Allen, “Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems , Oct 2012, pp. 1311–1317

    work page 2012

  17. [17]

    Semantic grasping: planning task-specific stable robotic grasps,

    H. Dang and P. K. Allen, “Semantic grasping: planning task-specific stable robotic grasps,” Autonomous Robots, vol. 37, no. 3, pp. 301–316, 2014

    work page 2014

  18. [18]

    Task-oriented grasping with seman- tic and geometric scene understanding,

    R. Detry, J. Papon, and L. Matthies, “Task-oriented grasping with seman- tic and geometric scene understanding,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 3266–3273

    work page 2017

  19. [19]

    Towards affor- dance detection for robot manipulation using affordance for parts and parts for affordance,

    S. R. Lakani, A. J. Rodr ´ıguez-S´anchez, and J. Piater, “Towards affor- dance detection for robot manipulation using affordance for parts and parts for affordance,” Autonomous Robots, vol. 43, no. 5, pp. 1155–1172, 2019

    work page 2019

  20. [20]

    Affordance detection of tool parts from geometric features,

    A. Myers, C. L. Teo, C. Ferm ¨uller, and Y . Aloimonos, “Affordance detection of tool parts from geometric features,” in 2015 IEEE Interna- tional Conference on Robotics and Automation (ICRA) . IEEE, 2015, pp. 1374–1381

    work page 2015

  21. [21]

    Dexterous grasping via eigen- grasps: A low-dimensional approach to a high-complexity problem,

    M. Ciocarlie, C. Goldfeder, and P. Allen, “Dexterous grasping via eigen- grasps: A low-dimensional approach to a high-complexity problem,” in Robotics: Science and Systems Manipulation Workshop-Sensing and Adapting to the Real World . Citeseer, 2007

    work page 2007

  22. [22]

    Pointnet: Deep learning on point sets for 3d classification and segmentation,

    C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 652–660

    work page 2017

  23. [23]

    Computational geometry algorithms library,

    C. Cgal, “Computational geometry algorithms library,” 2008

    work page 2008

  24. [24]

    Open3D: A Modern Library for 3D Data Processing

    Q.-Y . Zhou, J. Park, and V . Koltun, “Open3D: A modern library for 3D data processing,” arXiv:1801.09847, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  25. [25]

    Understanding tools: Task-oriented object modeling, learning and recognition,

    Y . Zhu, Y . Zhao, and S. Chun Zhu, “Understanding tools: Task-oriented object modeling, learning and recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 2855–2864

    work page 2015