Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911209210511360 |
|---|---|
| author | Kim, Chanwoo Yoon, Jihwan Kim, Hyeonseong Jeong, Taemoon Yoo, Changwoo Lee, Seungbeen Byeon, Soohwan Chung, Hoon Pan, Matthew Oh, Jean Lee, Kyungjae Choi, Sungjoon |
| author_facet | Kim, Chanwoo Yoon, Jihwan Kim, Hyeonseong Jeong, Taemoon Yoo, Changwoo Lee, Seungbeen Byeon, Soohwan Chung, Hoon Pan, Matthew Oh, Jean Lee, Kyungjae Choi, Sungjoon |
| contents | Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance of adaptability and safety. To this end, we develop a framework that learns a density-based reward from positive and negative demonstrations and augments it with rule-based objectives for obstacle avoidance and goal reaching. A sampling-based lookahead controller produces supervisory actions that are both safe and adaptive, which are subsequently distilled into a compact student policy suitable for real-time operation with uncertainty estimates. Experiments in synthetic and elevator co-boarding simulations show consistent gains in success rate and time efficiency over baselines, and real-world demonstrations with human participants confirm the practicality of deployment. A video illustrating this work can be found on our project page https://chanwookim971024.github.io/PioneeR/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications Kim, Chanwoo Yoon, Jihwan Kim, Hyeonseong Jeong, Taemoon Yoo, Changwoo Lee, Seungbeen Byeon, Soohwan Chung, Hoon Pan, Matthew Oh, Jean Lee, Kyungjae Choi, Sungjoon Robotics Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance of adaptability and safety. To this end, we develop a framework that learns a density-based reward from positive and negative demonstrations and augments it with rule-based objectives for obstacle avoidance and goal reaching. A sampling-based lookahead controller produces supervisory actions that are both safe and adaptive, which are subsequently distilled into a compact student policy suitable for real-time operation with uncertainty estimates. Experiments in synthetic and elevator co-boarding simulations show consistent gains in success rate and time efficiency over baselines, and real-world demonstrations with human participants confirm the practicality of deployment. A video illustrating this work can be found on our project page https://chanwookim971024.github.io/PioneeR/. |
| title | Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.12215 |