# Inference of Human-derived Specifications of Object Placement via Demonstration

arXiv: https://arxiv.org/abs/2508.19367 · HTML: https://mlsanity.com/paper/2508.19367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects](https://mlsanity.com/paper/2110.10189/) (2021)
- [Mining Parametric Temporal Logic Properties in Model Based Design for Cyber-Physical Systems](https://mlsanity.com/paper/1512.07956/) (2015)
- [Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration](https://mlsanity.com/paper/2105.14683/) (2021)
- [The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directions](https://mlsanity.com/paper/2010.14537/) (2020)
- [Little Tricky Logic: Misconceptions in the Understanding of LTL](https://mlsanity.com/paper/2211.01677/) (2022)
- [Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems](https://mlsanity.com/paper/2011.00384/) (2020)
- [Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera](https://mlsanity.com/paper/2012.08890/) (2020)

## Abstract (arXiv)

As robots' manipulation capabilities improve for pick-and-place tasks (e.g., object packing, sorting, and kitting), methods focused on understanding human-acceptable object configurations remain limited expressively with regard to capturing spatial relationships important to humans. To advance robotic understanding of human rules for object arrangement, we introduce positionally-augmented RCC (PARCC), a formal logic framework based on region connection calculus (RCC) for describing the relative position of objects in space. Additionally, we introduce an inference algorithm for learning PARCC specifications via demonstrations. Finally, we present the results from a human study, which demonstrate our framework's ability to capture a human's intended specification and the benefits of learning from demonstration approaches over human-provided specifications.