# Efficient and Trustworthy Social Navigation Via Explicit and Implicit Robot-Human Communication

arXiv: https://arxiv.org/abs/1810.11556 · HTML: https://mlsanity.com/paper/1810.11556/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [Efficient Trajectory Forecasting and Generation with Conditional Flow Matching](https://mlsanity.com/paper/2403.10809/) (2024)
- [Multi-Agent Curricula and Emergent Implicit Signaling](https://mlsanity.com/paper/2106.11156/) (2021)
- [Learning Social Navigation from Demonstrations with Conditional Neural Processes](https://mlsanity.com/paper/2210.03582/) (2022)
- [AdaptiX -- A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics](https://mlsanity.com/paper/2310.15887/) (2023)
- [Predicting Human Perceptions of Robot Performance During Navigation Tasks](https://mlsanity.com/paper/2310.11590/) (2023)
- [Enforcing Cybersecurity Constraints for LLM-driven Robot Agents for Online Transactions](https://mlsanity.com/paper/2503.15546/) (2025)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams](https://mlsanity.com/paper/2110.00751/) (2021)
- [DP-Dueling: Learning from Preference Feedback without Compromising User Privacy](https://mlsanity.com/paper/2403.15045/) (2024)
- [Reward Shaping with Subgoals for Social Navigation](https://mlsanity.com/paper/2104.06410/) (2021)
- [Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems](https://mlsanity.com/paper/2401.09473/) (2024)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)

## Abstract (arXiv)

In this paper, we present a planning framework that uses a combination of implicit (robot motion) and explicit (visual/audio/haptic feedback) communication during mobile robot navigation. First, we developed a model that approximates both continuous movements and discrete behavior modes in human navigation, considering the effects of implicit and explicit communication on human decision making. The model approximates the human as an optimal agent, with a reward function obtained through inverse reinforcement learning. Second, a planner uses this model to generate communicative actions that maximize the robot's transparency and efficiency. We implemented the planner on a mobile robot, using a wearable haptic device for explicit communication. In a user study of an indoor human-robot pair of orthogonal crossing situation, the robot was able to actively communicate its intent to users in order to avoid collisions and facilitate efficient trajectories. Results showed that the planner generated plans that were easier to understand, reduced users' effort, and increased users' trust of the robot, compared to simply performing collision avoidance. The key contribution of this work is the integration and analysis of explicit communication (together with implicit communication) for social navigation.