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The human-robot interaction community has developed many methods for robots to navigate safely and socially alongside humans. However, experimental procedures to evaluate these works are usually constructed on a per-method basis. Such disparate evaluations make it difficult to compare the performance of such methods across the literature. To bridge this gap, we introduce SocNavBench , a simulation framework for evaluating social navigation algorithms. SocNavBench comprises a simulator with photo-realistic capabilities and curated social navigation scenarios grounded in real-world pedestrian data. We also provide an implementation of a suite of metrics to quantify the performance of navigation algorithms on these scenarios. Altogether, SocNavBench provides a test framework for evaluating disparate social navigation methods in a consistent and interpretable manner. To illustrate its use, we demonstrate testing three existing social navigation methods and a baseline method on SocNavBench , showing how the suite of metrics helps infer their performance trade-offs. Our code is open-source, allowing the addition of new scenarios and metrics by the community to help evolve SocNavBench to reflect advancements in our understanding of social navigation.
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Stochastic Communication and Motion Planning via Learned Abstract World Representations
The increasing deployment of robots alongside humans necessitates sophisticated communication and motion planning to ensure safety and task achievability in social navigation scenarios. Existing methods often rely heavily on historical data and extensive expert hand-coding, which limits their scalability and generalizability. This paper introduces a novel framework that models social navigation as a Markov Decision Process (MDP), utilizing Conditional Abstraction Trees (CATs) to learn dynamic abstract world representations and policies that focus on critical aspects of interaction. In the offline phase, the framework operates within a simulator, while in the online phase, it deploys the learned representations and policies in real-world scenarios for ongoing refinement and adaptation. Integral to our approach is a Dynamic Bayesian Network (DBN) based human sensor and belief model that accounts for humans’ imperfect perception to enhance the prediction of human motion. We evaluated our method through extensive simulations and user studies involving physical experiments, demonstrating its effectiveness in managing critical interactions and ensuring safety and task completion across various scenarios.
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- Award ID(s):
- 2231620
- PAR ID:
- 10574107
- Publisher / Repository:
- CoRL
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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