# HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data

arXiv: https://arxiv.org/abs/2510.11321 · HTML: https://mlsanity.com/paper/2510.11321/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2025.

## Built on by (most cited first, 3 of 3)

- [SUREFlow: State-space Uncertainty-aware REsidual Flow Matching for Robust Robot Manipulation](https://mlsanity.com/paper/2607.10504/) (2026)
- [Remember Smarter: Visual History Compressor and Hyperbolic Experience Space for Robotic Memory](https://mlsanity.com/paper/2608.15269/) (2026)
- [Position: Unlabeled IS NOT Equal to No Human Supervision in Visual Learning](https://mlsanity.com/paper/2609.03077/) (2026)

## Abstract (arXiv)

Effective generalization in robotic manipulation requires representations that capture invariant patterns of interaction across environments and tasks. We present a self-supervised framework for learning hierarchical manipulation concepts that encode these invariant patterns through cross-modal sensory correlations and multi-level temporal abstractions without requiring human annotation. Our approach combines a cross-modal correlation network that identifies persistent patterns across sensory modalities with a multi-horizon predictor that organizes representations hierarchically across temporal scales. Manipulation concepts learned through this dual structure enable policies to focus on transferable relational patterns while maintaining awareness of both immediate actions and longer-term goals. Empirical evaluation across simulated benchmarks and real-world deployments demonstrates significant performance improvements with our concept-enhanced policies. Analysis reveals that the learned concepts resemble human-interpretable manipulation primitives despite receiving no semantic supervision. This work advances both the understanding of representation learning for manipulation and provides a practical approach to enhancing robotic performance in complex scenarios.