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Control-oriented Clustering of Visual Latent Representation
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Shows that behavior-cloned image-based controllers develop neural-collapse-like control-oriented clustering of visual features (RePo classes), and that NC-based pretraining of the vision encoder boosts test-time performance with few demonstrations.
Problem
Whether neural collapse-style clustering occurs in the visual representation space of image-based control policies learned by behavior cloning, where there are no natural class labels for continuous control, and whether it can be used to improve policy performance with limited expert demonstrations.
Approach
Defines control-oriented classes by splitting the relative pose (x, y, theta) between object and target (or the object's induced pose change under expert actions) into orthants (RePos), measures NC metrics (CDNV, STDNorm, STDAngle) on the visual features, and uses these NC metrics as a regularizer to pretrain the vision encoder before end-to-end finetuning.
Key results
- NC pretraining of ResNet encoder raises Push-T test score on letter O from 0.241 to 0.347 and on letter B from 0.202 to 0.360 (roughly 10% to 35% gains) with 100 demos.
- Real-world Franka pushing: NC-pretrained policy succeeded 8/10 vs 5/10 baseline with 100 demos on clean background; 9/10 vs 6/10 on visually complex background with 200 demos.
- Three NC metrics decrease toward zero during BC training on Lunar Lander, Push-T (ResNet and DINOv2 with diffusion model), and Block Stacking, indicating control-oriented clustering.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Pretrain the vision encoder with NC regularization using control-oriented labels | ResNet + Diffusion Model on low-data Planar Pushing · Planar Pushing, four ROBOT domains | test-time performance: 0.202 -> 0.360; 0.423 -> 0.720; 0.385 -> 0.504; 0.241 -> 0.347 | Helped |
| Pretrain the vision encoder with NC regularization using action-based labels | ResNet + Diffusion Model on low-data Planar Pushing · Planar Pushing, four ROBOT domains | test-time performance: 0.202 -> 0.378; 0.423 -> 0.769; 0.385 -> 0.488; 0.241 -> 0.351 | Helped |
| Pretrain the vision encoder with NC regularization | DINOv2 + Diffusion Model on low-data Planar Pushing · Planar Pushing, four ROBOT domains | test-time performance: 0.1895 -> 0.2110; 0.3747 -> 0.4602; 0.2463 -> 0.2466; 0.1942 -> 0.2468 | Helped |
| Use ImageNet-pretrained rather than randomly initialized ResNet, with the encoder frozen (ablation) | ResNet + Diffusion Model · Planar Pushing, clean and visually complex backgrounds | performance (%): 29.95 -> 30.17; 32.22 -> 32.90 | No clear effect |
| Use ImageNet-pretrained rather than randomly initialized ResNet, with end-to-end finetuning (ablation) | ResNet + Diffusion Model · Planar Pushing, clean and visually complex backgrounds | performance (%): 63.66 -> 62.83; 46.67 -> 56.10 | Mixed |
| Replace the Diffusion Model action decoder with LSTM (ablation) | Planar Pushing image-based control pipeline · Planar Pushing | test-time performance | Hurt |
| Use k-means clusters of output actions instead of control-oriented relative-pose classes (ablation) | ResNet + Diffusion Model · Planar Pushing | CDNV | Hurt |
| Increase the number of relative-pose classes from 8 to 64 or 216 (ablation) | ResNet + Diffusion Model with goal-based classification · Planar Pushing | CDNV: 8, 64, 216 | Mixed |
| Use goal-based instead of action-based relative-pose classification (ablation) | ResNet + Diffusion Model · Planar Pushing | CDNV | Helped |
| Use NC-pretrained rather than baseline policy | ResNet + Diffusion Model · Real-world Planar Pushing, clean background, 100 demonstrations | successful tests: 5 out of 10 -> 8 out of 10 | Helped |
| Use NC-pretrained rather than baseline policy | ResNet + Diffusion Model · Real-world Planar Pushing, visually complex background, 200 demonstrations | successful tests: 6 out of 10 -> 9 out of 10 | Helped |
| Use NC-pretrained rather than baseline policy | ResNet + Diffusion Model · Real-world Planar Pushing, clean background, 50 demonstrations | successful tests: 2 out of 10 -> 4 out of 10 | Helped |
Surprising findings
- k-means clustering of expert actions does not produce neural collapse, whereas relative-pose-orthant classes do.
- ImageNet-pretrained ResNet gives no clear benefit over random initialization on clean Push-T when finetuned; frozen ResNets perform poorly.
Limitations
- NC observation and NC pretraining require ground-truth relative poses during training, hard to scale to multiple or deformable objects
- Theoretical analysis of control-oriented clustering is absent; the linear-classifier assumption of NC theory is violated
- Real-world experiments are small (10 trials per setting)
Experimental setup
Datasets: Push-T simulated demonstrations (pymunk) MimicGen core stack_d0 Lunar Lander expert demos real-world Franka pushing demos
Benchmarks: Push-T simulated planar pushing (100 test tasks) Block Stacking (MimicGen) Lunar Lander real-world Franka planar pushing
Metrics: overlap area ratio (test score) CDNV STDNorm STDAngle success rate
Baselines: ResNet+DM baseline DINOv2+DM LSTM action decoder k-means action clustering
Builds on: neural collapse Diffusion Policy DINOv2
Closest papers by meaning
- The Unsurprising Effectiveness of Pre-Trained Vision Models for Control (ICML 2022 · similarity 0.87)
- DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors (NeurIPS 2024 · similarity 0.86)
- Live and Learn: Continual Action Clustering with Incremental Views (AAAI 2024 · similarity 0.85)
- Neural Clustering based Visual Representation Learning (CVPR 2024 · similarity 0.85)
- Vision Transformers Learn Gestalt-Like Figure-Ground Cues from Natural Images (NeurIPS 2026 · similarity 0.84)
- Learning to cluster neuronal function (NeurIPS 2025 · similarity 0.84)
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