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arxiv: 1907.10932 · v1 · pith:QSZGCK4Snew · submitted 2019-07-25 · 💻 cs.RO

Object Perception and Grasping in Open-Ended Domains

S. Hamidreza Kasaei This is my paper

Pith reviewed 2026-05-24 16:31 UTC · model grok-4.3

classification 💻 cs.RO
keywords open-ended learningobject recognitiongrasp affordancesservice robotsincremental learninginteractive learningvisual perceptioncognitive robotics
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The pith

Robots need open-ended learning to recognize unknown objects and their grasp affordances as categories and instances arrive gradually over time.

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

The paper establishes that service robots entering unstructured human environments require open-ended learning for object perception and grasping. This means the robot processes visual data and builds knowledge of object categories and grasp affordances without a predefined set of classes, instead drawing training instances incrementally from its own ongoing experiences and human interactions. The work examines why this matters for autonomy, how incremental learning from experience and interaction can work, the limits of deep learning in such settings, and suitable evaluation metrics. A sympathetic reader would see this as necessary for robots to adapt continuously rather than relying on static training data.

Core claim

An autonomous robot must have the ability to process visual information and conduct learning and recognition tasks in an open-ended fashion where the set of object categories is not known in advance and training instances become gradually available over time rather than being completely available at the beginning of the learning process. This capability, inspired by human ceaseless learning of object categories and grasp affordances, enables adaptation to new environments through accumulation of experiences and conceptualization of new categories.

What carries the argument

Interactive open-ended learning approaches that recognize multiple objects and their grasp affordances concurrently by accumulating experiences incrementally.

If this is right

  • Robots can adapt to new environments by enhancing knowledge from accumulated experiences rather than requiring all data upfront.
  • Robots can learn incrementally from their own experiences as well as from direct interaction with humans.
  • Deep learning approaches have specific limitations when applied in an open-ended manner with gradually available data.
  • Open-ended learning approaches require dedicated evaluation methods and metrics distinct from standard batch learning benchmarks.

Where Pith is reading between the lines

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

  • Such systems would allow service robots to operate long-term in homes or offices where novel items continue to appear without retraining from scratch.
  • Evaluation protocols might need to incorporate lifelong interaction logs rather than isolated test sets to measure adaptation over extended periods.
  • Alternative learning paradigms beyond current deep networks could become necessary if incremental updates prove unstable in practice.

Load-bearing premise

Cognitive science observations of how humans learn object categories and grasp affordances ceaselessly translate into a direct requirement that robots must use the same incremental, experience-driven process.

What would settle it

A controlled test in which a robot using non-incremental batch learning on a fixed dataset maintains or exceeds performance on new object categories that appear gradually through online robot experiences.

Figures

Figures reproduced from arXiv: 1907.10932 by S. Hamidreza Kasaei.

Figure 1
Figure 1. Figure 1: Four examples of affordance detection results. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed OrthographicNet. B. Grasp Affordance Learning and Recognition Robots are still not able to grasp all unforeseen objects and finding a proper grasp configuration, i.e. the position and orientation of the arm relative to the object, is still challenging [8, 23, 12]. One approach for grasping unforeseen objects is to recognize an appropriate grasp configuration from previous grasp dem… view at source ↗
read the original abstract

Nowadays service robots are leaving the structured and completely known environments and entering human-centric settings. For these robots, object perception and grasping are two challenging tasks due to the high demand for accurate and real-time responses. Although many problems have already been understood and solved successfully, many challenges still remain. Open-ended learning is one of these challenges waiting for many improvements. Cognitive science revealed that humans learn to recognize object categories and grasp affordances ceaselessly over time. This ability allows adapting to new environments by enhancing their knowledge from the accumulation of experiences and the conceptualization of new object categories. Inspired by this, an autonomous robot must have the ability to process visual information and conduct learning and recognition tasks in an open-ended fashion. In this context, "open-ended" implies that the set of object categories to be learned is not known in advance, and the training instances are extracted from online experiences of a robot, and become gradually available over time, rather than being completely available at the beginning of the learning process. In my research, I mainly focus on interactive open-ended learning approaches to recognize multiple objects and their grasp affordances concurrently. In particular, I try to address the following research questions: (i) What is the importance of open-ended learning for autonomous robots? (ii) How robots could learn incrementally from their own experiences as well as from interaction with humans? (iii) What are the limitations of Deep Learning approaches to be used in an open-ended manner? (iv) How to evaluate open-ended learning approaches and what are the right metrics to do so?

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 / 0 minor

Summary. The manuscript is a research statement outlining the author's focus on interactive open-ended learning for concurrent object recognition and grasp affordance prediction in autonomous robots. It draws motivation from cognitive science findings on human incremental learning, asserts that robots must process visual information and learn in an open-ended manner (where object categories are unknown in advance and training data arrives gradually from online experiences), and lists four research questions to be addressed: the importance of open-ended learning, incremental learning from robot experiences and human interaction, limitations of deep learning in open-ended settings, and appropriate evaluation metrics.

Significance. The topic of open-ended robotic learning is relevant to service robotics in unstructured human environments. However, the manuscript contains no methods, algorithms, experiments, derivations, or results. If the posed questions were later answered with reproducible implementations and evaluations, the work could contribute to robotics; as presented, it offers no assessable advance.

major comments (2)
  1. [Abstract] Abstract and research questions section: The manuscript poses four open research questions but provides no technical approach, algorithm, dataset, or evaluation to address any of them. This absence means the document functions as a statement of intent rather than a completed study with load-bearing claims or evidence.
  2. [Abstract] Abstract: The assertion that 'an autonomous robot must have the ability to process visual information and conduct learning and recognition tasks in an open-ended fashion' is presented as a premise without supporting argument, comparison to alternative paradigms, or empirical grounding within the manuscript.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their review. This manuscript is a research statement that outlines a research agenda and poses open questions on interactive open-ended learning for robot object perception and grasping, motivated by cognitive science. We respond point by point to the major comments.

read point-by-point responses

  1. Referee: [Abstract] Abstract and research questions section: The manuscript poses four open research questions but provides no technical approach, algorithm, dataset, or evaluation to address any of them. This absence means the document functions as a statement of intent rather than a completed study with load-bearing claims or evidence.

    Authors: We agree that the manuscript contains no new algorithms, datasets, or experimental results. It is intentionally structured as a research statement to define the problem space and research questions rather than to present a completed empirical study. The contribution is in framing the open-ended learning challenge for service robots based on cognitive science insights and identifying directions for future work. revision: no

  2. Referee: [Abstract] Abstract: The assertion that 'an autonomous robot must have the ability to process visual information and conduct learning and recognition tasks in an open-ended fashion' is presented as a premise without supporting argument, comparison to alternative paradigms, or empirical grounding within the manuscript.

    Authors: The premise follows directly from the preceding sentences that reference cognitive science findings on human incremental learning of categories and affordances over time. The text contrasts this with the standard robotics assumption of complete upfront training sets. While the manuscript does not introduce new empirical comparisons, the argument is grounded in the cited cognitive science motivation. revision: no

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

This is a research proposal that poses four open research questions rather than presenting any derivation, equations, predictions, or fitted quantities. The central premise that robots must adopt incremental open-ended learning is stated as an inspiration drawn from cognitive science, not derived or fitted within the document. No self-citations, ansatzes, or renamings of results appear as load-bearing steps. The paper is self-contained as a statement of research intent with no internal reduction of claims to their own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced because the text is a research agenda summary without technical derivations.

pith-pipeline@v0.9.0 · 5807 in / 964 out tokens · 18577 ms · 2026-05-24T16:31:37.581848+00:00 · methodology

discussion (0)

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Reference graph

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