Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning (1809.08835v2)

Abstract: Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, their cooperation ability deteriorates as the crowd grows since they typically relax the problem as a one-way Human-Robot interaction problem. In this work, we want to go beyond first-order Human-Robot interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework. Our model captures the Human-Human interactions occurring in dense crowds that indirectly affects the robot's anticipation capability. Our proposed attentive pooling mechanism learns the collective importance of neighboring humans with respect to their future states. Various experiments demonstrate that our model can anticipate human dynamics and navigate in crowds with time efficiency, outperforming state-of-the-art methods.

• The paper introduces an attention mechanism that models both Human-Robot and Human-Human interactions to enhance navigation in crowded environments.
• It employs an attentive pooling strategy that dynamically prioritizes key agents, improving the robot's decision-making and path selection.
• Simulations show 100% success rates with no collisions and reduced discomfort, outperforming existing state-of-the-art methods.

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

The paper presents a novel approach for robot navigation within crowded environments through an advanced framework termed Crowd-Robot Interaction (CRI). This framework is designed to enhance robots' capability to traverse complex social spaces in a cooperative and socially compliant manner. By employing an attention-based deep reinforcement learning methodology, this work seeks to transcend traditional first-order Human-Robot interaction paradigms and incorporates explicit modeling of both Human-Robot and Human-Human interactions.

Key Contributions

1. Self-Attention Mechanism for Pairwise Interactions: The research introduces an innovative approach by integrating a self-attention mechanism to rethink and manage the pairwise Human-Robot interactions. This mechanism captures nuanced interactions and prioritizes them based on their relative importance.
2. Human-Human Interactions: A distinguishing feature of the proposed model is its ability to incorporate Human-Human interactions within a crowd, which indirectly affect the robot's navigation decisions. This holistic consideration of interactions enhances the robot's anticipation and decision-making capabilities in densely populated environments.
3. Attentive Pooling Mechanism: To handle varying numbers of agents, the model includes an attentive pooling mechanism that calculates the collective significance of neighboring humans based on their future states. This allows the model to dynamically adjust its focus and adapt its navigation strategy accordingly.
4. Superior Performance Metrics: Through extensive simulation experiments, the proposed model demonstrated a superior ability to anticipate human dynamics and achieve time-efficient navigation compared to state-of-the-art methods, establishing itself as a robust framework for CRI.

Quantitative and Qualitative Outcomes

The paper highlights significant empirical results. In scenarios where the robot is invisible to the crowd, the proposed model, both with (LM-SARL) and without local maps (SARL), achieved a 100% success rate with no collisions, while outperforming existing methods in terms of navigation time and reward metrics. In the visible robot scenarios, the model maintained high success rates and optimal rewards while exhibiting significantly reduced discomfort frequencies.

Qualitatively, analysis outlined the LM-SARL's ability to assess and respond to dynamic crowd conditions more effectively, evident in its intelligent path selection that prioritized safety and efficiency. The attention mechanism allowed the model to discern the most influential agents in complex interactions, thereby adopting paths that adeptly avoided potential bottlenecks.

Theoretical and Practical Implications

The proposed model's ability to holistically integrate Human-Human and Human-Robot interactions within the navigation framework provides a significant leap forward in the field of autonomous navigation in social environments. This integration allows robots to emulate human-like navigation strategies, potentially paving the way for seamless human-robot coexistence in shared spaces such as malls, airports, and pedestrian pathways.

Furthermore, the application of self-attention mechanisms introduces a flexible and scalable approach to modeling interactions in multi-agent systems. This lays the groundwork for future research in improving interaction modeling efficiency and accuracy in similar domains.

Future Directions

Future explorations could enhance the robustness and scalability of the model by incorporating additional sensory inputs, such as advanced vision systems, to improve the environmental perception of robots. Expanding the framework to consider varying environmental layouts and external factors might increase its applicability across diverse real-world scenarios. Additionally, deploying this model on various robotic platforms could validate its utility in practical applications.

Overall, the paper presents a significant contribution to the domain of autonomous robot navigation, offering robust solutions for crowd-aware navigation through its innovative attention-based deep reinforcement learning framework.