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Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors

Lingfeng Sun, Yixiao Wang, Pin-Yun Hung, Changhao Wang, Xiang Zhang, Zhuo Xu, Masayoshi Tomizuka

TL;DR

This paper introduces a novel framework utilizing distributed potential games to simulate human-like interactions in highly interactive scenarios and develops a gym-like environment leveraging the interactive agent model to facilitate the learning and evaluation of interactive navigation algorithms.

Abstract

Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance and collaborative interaction, such as indoor spaces. Unlike static or predictably moving obstacles, human behavior is inherently complex and unpredictable, stemming from dynamic interactions with other agents. Existing simulation tools frequently fail to adequately model such reactive and collaborative behaviors, impeding the development and evaluation of robust social navigation strategies. This paper introduces a novel framework utilizing distributed potential games to simulate human-like interactions in highly interactive scenarios. Within this framework, each agent imagines a virtual cooperative game with others based on its estimation. We demonstrate this formulation can facilitate the generation of diverse and realistic interaction patterns in a configurable manner across various scenarios. Additionally, we have developed a gym-like environment leveraging our interactive agent model to facilitate the learning and evaluation of interactive navigation algorithms.

Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors

TL;DR

This paper introduces a novel framework utilizing distributed potential games to simulate human-like interactions in highly interactive scenarios and develops a gym-like environment leveraging the interactive agent model to facilitate the learning and evaluation of interactive navigation algorithms.

Abstract

Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance and collaborative interaction, such as indoor spaces. Unlike static or predictably moving obstacles, human behavior is inherently complex and unpredictable, stemming from dynamic interactions with other agents. Existing simulation tools frequently fail to adequately model such reactive and collaborative behaviors, impeding the development and evaluation of robust social navigation strategies. This paper introduces a novel framework utilizing distributed potential games to simulate human-like interactions in highly interactive scenarios. Within this framework, each agent imagines a virtual cooperative game with others based on its estimation. We demonstrate this formulation can facilitate the generation of diverse and realistic interaction patterns in a configurable manner across various scenarios. Additionally, we have developed a gym-like environment leveraging our interactive agent model to facilitate the learning and evaluation of interactive navigation algorithms.

Paper Structure

This paper contains 29 sections, 1 theorem, 15 equations, 10 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries and Background
  3. Distributed Multi-agent Planning
  4. Potential Game
  5. Imagined Potential Game for Collaborative Interaction Generation
  6. Improvements on Interaction generation for vanilla IPG
  7. Results
  8. Learning to Interact with Collaborative Agents using IPG
  9. Environment settings
  10. Reinforcement learning setting and Initial Results
  11. Evaluating interaction generation and interactive agents
  12. Related Works
  13. Conclusion
  14. Potential Game
  15. Formulation
  16. ...and 14 more sections

Key Result

Theorem 1

For a given differential game $\Gamma_{x_0}^T=(N, \{U_i\}_{i=1}^N, \{J_i\}_{i=1}^N, \{f_i\}_{i=1}^N)$, if for each agent $i$, the running cost and terminal cost functions have the structure of then the open-loop Nash equilibria can be found by solving the following optimal control problem

Figures (10)

  • Figure 1: A human-like interaction at the T-intersection: one agent stepped back to yield.
  • Figure 2: Selected interaction trajectories from the generated closed-loop interactions in Hallway and Intersection scenarios using randomized configurations. Cooperative predictions are hidden for clearness in interactions of more than two agents.
  • Figure 3: IPG agents interact with different types of agents.
  • Figure 4: Interaction zones in Hallway and Intersection.
  • Figure 5: Reconstructed real interactions (dashed) using IPG.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Definition 1
  • Theorem 1