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Knolling Bot: Teaching Robots the Human Notion of Tidiness

Conference: NeurIPS 2025 arXiv: 2310.04566 Code: https://github.com/yuhanghu/knolling (available, includes dataset and benchmark) Area: Robotics Keywords: knolling, object arrangement, Transformer, GMM, self-supervised learning, autoregressive generation

TL;DR

This work frames desktop object tidying (knolling) as an NLP-style sequence prediction task, employing a Transformer to autoregressively generate target poses for each object. A Gaussian Mixture Model (GMM) handles solution ambiguity, the model is trained on 2.4 million automatically generated demonstrations to learn a generalizable notion of tidiness, and user preferences are implicitly encoded via the input ordering of objects.

Background & Motivation

Limitations of Prior Work

A core challenge for domestic service robots is understanding the subjective human notion of "tidiness." Unlike standardized industrial settings, home environments involve diverse and varying numbers of objects, and what constitutes "neat arrangement" differs across individuals.

State of the Field

Subjectivity and multi-modality: There is no single correct answer to "tidy." A given set of objects can be reasonably grouped by color, sorted by size, or organized by category — all are valid arrangements. This is not a standard regression problem with a unique ground-truth target.

Starting Point

Failure of regression approaches: A naive regression model predicting each object's target position will, when multiple valid arrangements coexist, learn their average — potentially placing objects at geometrically intermediate positions (e.g., on top of another object), yielding completely unreasonable results. Figure 2A of the paper illustrates this problem clearly.

Root Cause

Variable-length input: The number of objects is not fixed, ranging from 2 to 10 or more; the model must handle arbitrarily long inputs.

Paper Goals

Scalability bottleneck of rule-based methods: Traditional rule-based approaches suffer from combinatorial complexity as scene diversity grows, whereas learning-based methods can continuously benefit from more data.

Key analogy: Object arrangement ≈ language generation. Objects are "words" and an arrangement is a "sentence" — the same set of words can form multiple meaningful sentences. This analogy naturally motivates the adoption of the Transformer autoregressive framework from NLP.

Method

Overall Architecture

The system is decomposed into three decoupled modules:

  1. Knolling Model (cognitive layer): A Transformer that autoregressively predicts the target pose of each object.
  2. Visual Perception Model (perception layer): A customized YOLOv8 detector that identifies objects and extracts their dimensions and current poses.
  3. Robot Arm Controller (execution layer): Drives a WidowX 200 five-DOF manipulator to perform pick-and-place operations.

The three modules are independently designed and replaceable, improving system maintainability and interpretability.

Key Designs

  1. Pure geometric input representation: The model takes only object width (\(w\)) and length (\(l\)) as input features, deliberately excluding semantic attributes such as color and category. The design motivation is twofold: (a) geometric properties are objective and quantifiable, free from cultural or personal bias; and (b) preference information is injected via input ordering (see point 3) rather than mixed into the feature space. This separation enables the model to learn a more generalizable notion of tidiness.

  2. Transformer + GMM autoregressive prediction: The model predicts target positions sequentially, object by object; previously predicted positions are fed back as conditioning context for subsequent predictions (autoregressive). Each step outputs not a fixed coordinate but a GMM distribution — multiple Gaussian components each represent a distinct plausible placement. Sampling from the GMM yields target coordinates while naturally avoiding the mode-averaging problem of regression. The key advantage of the multi-modal GMM output is that, when multiple valid arrangements coexist, the model produces multiple distribution peaks rather than a single averaged value.

  3. Preference as ordering: User preferences are not encoded as extra input dimensions but are implicitly conveyed through the input ordering of objects. Providing objects sorted by color → the model generates a color-grouped arrangement; sorted by size → size-grouped; sorted by category → category-grouped. This design is remarkably elegant — it requires no modification to the model architecture, no preference annotations, and no additional training; only the input ordering at inference time is changed to control the arrangement style.

Data Generation & Training

Data generation (2.4 million demonstrations): An optimization procedure iteratively adjusts object positions to minimize workspace footprint. By controlling ordering and randomizing parameters, multiple arrangement styles are generated for each object combination. The entire process is fully automatic, requiring no manual annotation.

Two-stage curriculum training:

  • Stage 1 — Self-supervised pre-training: Following BERT-style masked learning, a random subset of object information is masked and the model learns to predict the masked positions, thereby acquiring fundamental spatial regularities of object arrangements.
  • Stage 2 — Knolling task fine-tuning: The model is fine-tuned on complete from-scratch knolling tasks to improve end-to-end performance on the full arrangement objective.

Irregular object handling: For irregularly shaped objects (e.g., 3D-printed parts), the visual perception system performs segmentation and computes the minimum bounding rectangle, converting the shape into a rectangular \((w, l)\) representation.

Key Experimental Results

Simulation Experiments

  • The model successfully generates tidy arrangements for object counts ranging from 2 to 10.
  • The natural variable-length handling of the Transformer autoregressive framework eliminates the need for separate models for different object counts.
  • By varying the input ordering, three distinct arrangement styles (grouped by color, category, and size) are successfully generated for the same set of objects.

Real Robot Experiments

  • Hardware: WidowX 200 five-DOF manipulator + Intel RealSense D435 depth camera (mounted top-down).
  • Scene setup: 6–10 objects of varying sizes and colors (blocks and everyday items) are randomly placed within the robot's workspace.

Full pipeline validation:

  1. Top-down camera captures the scene image.
  2. Customized YOLOv8 detects objects and extracts dimensions \((w, l)\) and current poses.
  3. The Knolling Model predicts the target position for each object.
  4. The robot arm controller plans and executes the pick-and-place sequence.
  5. The final desktop presents a tidy arrangement.

Tests across different object configurations (6/8/10 objects) and different preferences (by color/category/shape) all succeed.

Ablation Study

  • Transformer vs. baseline architectures: The Transformer consistently outperforms MLP and CNN baselines; self-attention provides a decisive advantage in capturing inter-object spatial relationships.
  • GMM vs. standard regression: GMM effectively avoids mode averaging under multi-modal conditions, yielding significant quantitative improvements over L2 regression.
  • Data scaling effect: Performance improves continuously with more training data — in sharp contrast to rule-based methods whose complexity explodes with scene diversity.

Key Findings

  • NLP techniques (Transformer autoregression + masked learning) can be successfully transferred to robotic object arrangement tasks.
  • Implicitly encoding preferences via input ordering is both effective and general — the model automatically learns that "ordering implies grouping."
  • Using purely geometric inputs (excluding semantic attributes) does not hurt performance; it actually improves generalization.
  • 2.4 million self-supervisedly generated demonstrations are sufficient for the model to learn a robust notion of tidiness.

Highlights & Insights

  • Cross-domain analogy: Framing object arrangement as sequence generation is the paper's central contribution — concise, powerful, and naturally motivating the Transformer + autoregressive technical solution.
  • Preference as ordering: Encoding user preferences through input ordering is an elegant design. No extra dimensions, no preference annotations, no architectural changes — only the data ordering at inference time is modified. This "free" control mechanism is impressive.
  • Cognitive–perceptual–execution decoupling: The modular system design allows each component to be independently improved and replaced, serving as a strong example of good engineering practice.
  • Scalable self-supervision: 2.4 million fully automatically generated arrangement demonstrations, requiring no manual annotation, with diverse and high-quality training data produced via algorithmic optimization.

Limitations & Future Work

  1. 2D scenes only: The top-down \((w, l)\) representation cannot handle object stacking or 3D spatial arrangement.
  2. Shape simplification: Approximating object shapes with minimum bounding rectangles limits performance for highly irregular objects (e.g., clothing, cables).
  3. Fixed workspace: Arrangements are restricted to the robot's reachable tabletop area; the approach does not scale to room-level tidying.
  4. Limited preference expressiveness: Input ordering can only encode grouping preferences based on a single attribute; more complex tidying rules (e.g., "frequently used items within reach," "hazardous items away from edges") cannot be expressed.
  5. No interactive feedback: The system cannot adjust its plan based on real-time user feedback during execution.
  6. Evaluation criteria: Systematic quantitative comparison between the model's tidiness metric and human subjective judgment is lacking.
Method Characteristics Distinction from This Work
Housekeep (Kant et al. 2022) LLM commonsense-driven tidying Relies on language instructions and predefined rules
TIDEE (Sarch et al. 2022) Visual-semantic prior for room tidying Room-level scenes, not desktop knolling
StructFormer (Liu et al. 2022) Language-guided semantic rearrangement Requires explicit language goal descriptions
My House, My Rules (Kapelyukh 2022) GNN learning tidying preferences Requires explicit preference annotations
Ours Self-supervised sequence prediction Preferences implicitly encoded via input ordering
  • Cross-domain inspiration: A successful instance of transferring NLP techniques to physical-world tasks — can other abstract concepts (e.g., "aesthetics," "comfort") also be modeled as sequence problems?
  • Multi-modal output: The GMM mechanism for handling one-to-many mappings generalizes to other robotic tasks with multiple valid solutions, such as path planning and grasp strategy selection.

Rating

⭐⭐⭐⭐ (4/5)

The cross-domain analogy from object arrangement to sequence prediction is novel and compelling, and the implicit preference encoding via input ordering is elegantly simple. The complete closed-loop validation combining 2.4 million self-supervised demonstrations with real robot deployment is convincing. Points are deducted for the restriction to 2D tabletop scenes, limited preference expressiveness, and the absence of quantitative comparison against human tidiness judgments.