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Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement
Mingyue Luo1, Hui Li2, Wanbo Luo3,4
1School of Mechatronic Engineering, Changchun University of Technology, Yan'an St., Changchun, 130012, Jilin Province, China.
Scientific Reports
|July 30, 2025
Summary
This study introduces a goal-oriented autonomous decision-making (GO-ADM) method for social robots, improving navigation by learning from expert demonstrations without predicting pedestrian paths. The GO-ADM method ensures safe and efficient navigation, even with interference.
Area of Science:
- Robotics
- Artificial Intelligence
- Human-Robot Interaction
Background:
- Existing inverse reinforcement learning (IRL) for social robots often relies on trajectory planning, which is impractical for robots with clear goals and struggles with long-distance predictions.
- Current methods face limitations due to unknown pedestrian goal information and the complexity of predicting trajectories, especially at longer distances.
- Social robots require robust navigation strategies that account for social norms and safety.
Purpose of the Study:
- To propose a novel goal-oriented autonomous decision-making (GO-ADM) method for social robots.
- To address the social compatibility navigation problem by enabling robots to make sequential decisions without pedestrian trajectory prediction.
- To enhance human-robot interaction safety through social distance considerations.
Main Methods:
- Developed a goal-oriented autonomous decision-making (GO-ADM) framework using sequential actions at discrete time steps.
- Defined and collected goal-oriented expert demonstrations for training.
- Proposed a collaborative interactive inverse reinforcement learning (IRL) framework, integrating explicit and implicit pedestrian motion norms into a goal-oriented reward function and a social safety distance penalty.
Main Results:
- The GO-ADM method achieved rational autonomous decisions in both longitudinal and lateral dominant navigation tasks, with average destination deviations under 0.13m and 0.23m, respectively.
- Demonstrated significantly higher success rates compared to other social navigation and decision-making algorithms.
- Exhibited strong robustness against unknown interferences, achieving over 75% success rate under severe conditions (0.5m noise).
Conclusions:
- The proposed GO-ADM method effectively solves the social compatibility navigation problem for robots with clear goals.
- The approach eliminates the need for complex pedestrian trajectory prediction, offering a more practical solution.
- GO-ADM ensures safe, efficient, and robust navigation in human-robot interaction scenarios.
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