# Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding

arXiv: https://arxiv.org/abs/2607.10372 · HTML: https://mlsanity.com/paper/2607.10372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 161 references that are in the map.
- First posted on arXiv 11 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification](https://mlsanity.com/paper/2001.01526/) (2020)
- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks](https://mlsanity.com/paper/1703.03098/) (2017)
- [VBR: A Vision Benchmark in Rome](https://mlsanity.com/paper/2404.11322/) (2024)
- [Open-Vocabulary Online Semantic Mapping for SLAM](https://mlsanity.com/paper/2411.15043/) (2024)
- [Semantic Mapping in Indoor Embodied AI -- A Survey on Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2501.05750/) (2025)
- [OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding](https://mlsanity.com/paper/2503.01646/) (2025)
- [Monocular SLAM Supported Object Recognition](https://mlsanity.com/paper/1506.01732/) (2015)
- [Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics](https://mlsanity.com/paper/2201.13066/) (2022)
- [Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey](https://mlsanity.com/paper/2505.12384/) (2025)
- [Object-level 3D Semantic Mapping using a Network of Smart Edge Sensors](https://mlsanity.com/paper/2211.11354/) (2022)
- [Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps](https://mlsanity.com/paper/2501.07399/) (2025)
- [WoLF: the Whole-body Locomotion Framework for Quadruped Robots](https://mlsanity.com/paper/2205.06526/) (2022)
- [Semantic-based Loco-Manipulation for Human-Robot Collaboration in Industrial Environments](https://mlsanity.com/paper/2312.14487/) (2023)

## Abstract (arXiv)

The transition of autonomous mobile robots from controlled industrial settings to dynamic, human-centric environments, such as manufacturing, logistics, and healthcare, has made their safe and autonomous operation a critical area of research. These sophisticated machines must be capable of perceiving, understanding, and interacting with their surroundings to navigate freely and perform complex tasks. A significant obstacle to achieving this is the lack of comprehensive contextual awareness, which requires a robot to recognize its spatial environment and identify the objects and actors within it. Without this perceptual knowledge, robots struggle to plan adaptive behaviors or engage in meaningful interaction with humans. This thesis presents novel solutions to this challenge by exploring two distinct but complementary research directions. The first direction involves human re-identification and tracking to improve Human-Robot Collaboration. Our developed approach enables a mobile robot to recognize a specific person, facilitating targeted collaboration while ignoring other individuals. The second direction focuses on enhancing the robot's overall perceptual capabilities to understand its environment geometrically and semantically. Geometric information is vital for motion planning and collision avoidance, while semantic knowledge provides the robot with a richer understanding for more advanced interaction. Both solutions are driven by the improvement of the semantical understanding of robots that enhance their knowledge of their surroundings, allowing a smoother and more natural interaction between robots, humans, and the environment. The contributions of this work in human re-identification and environmental understanding represent a significant step toward a future where robots are more contextually aware, enabling safer coexistence and more effective collaboration.