Social Navigation Path Planning Using Game-Theoretic Joint States
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Solution Overview
Problem
Existing navigation strategies for robots in human-occupied spaces often lead to aggressive or over-conservative behavior due to misjudgment of human-human and human-robot interactions, as robots incorrectly assess whether humans will make space for them, resulting in interference and inefficient path planning.
Innovation Solution
A computer-implemented method for game-theoretic path planning that identifies dynamic agents in an environment, determines preference distributions for each agent, and applies a recursive model to calculate trajectory likelihoods based on expected interference risk, minimizing deviations from the goal state and interference risk, thereby optimizing path planning without explicit communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional navigation strategies are used, then path planning is simple, but navigation accuracy and social appropriateness deteriorate due to misjudgment of human interactions
Solution Approach 1:
The patent transforms the path planning problem by changing parameters from deterministic trajectories to probability distributions over trajectories. This allows the robot to represent uncertainty about human intentions and compute Nash equilibria in continuous spaces, improving navigation accuracy while managing complexity through parameter transformation
Solution Approach 2:
The patent introduces an intermediary layer of game-theoretic reasoning that mediates between simple path planning and complex human interaction prediction. By using Nash equilibrium computation as an intermediary, the system achieves accurate social navigation without requiring direct complex modeling of all human behaviors
2Productivity
If robots assume humans will make space, then path planning is aggressive and fast, but reliability deteriorates due to wrong expectations
Solution Approach 1:
The patent makes the navigation strategy dynamic by computing Nash equilibria that adapt to the current joint state of all agents. The probability distributions over trajectories are updated recursively, allowing the robot to dynamically adjust between aggressive and conservative behaviors based on real-time human responses and inferred intentions
Solution Approach 2:
The patent implements feedback through recursive computation of trajectory likelihoods based on expected interference risk. The system continuously monitors human reactions and updates its belief about human intentions, adjusting path planning in real-time to maintain both speed and reliability through closed-loop control
3Reliability
If robots assume no human cooperation, then interaction reliability is conservative and safe, but productivity deteriorates due to over-conservative behavior
Solution Approach 1:
The patent changes the parameter representation from binary assumptions (cooperative or not) to continuous probability distributions over trajectories. This allows the robot to express nuanced beliefs about human cooperation likelihood, enabling both reliable and efficient navigation by selecting trajectories that optimize the trade-off based on inferred human intentions
Solution Approach 2:
The patent applies partial action by computing trajectory likelihoods that consider only the necessary level of cooperation needed for efficient navigation. Rather than assuming full cooperation or complete non-cooperation, the system computes partial trajectories that achieve navigation goals with the minimal necessary human assistance, improving productivity while maintaining reliability
4Measurement precision
If complex game theoretic models are used, then navigation accuracy improves, but computational power requirements increase
Solution Approach 1:
The patent segments the complex game-theoretic computation into manageable components: (1) representing preferences as probability distributions, (2) computing trajectory likelihoods recursively, and (3) updating beliefs about human intentions iteratively. This segmentation enables accurate social navigation while reducing computational energy through modular processing
Solution Approach 2:
The patent computes only the necessary portion of the game-theoretic solution required for navigation decisions. By calculating trajectory likelihoods based on expected interference risk rather than full equilibrium computation in all possible scenarios, the system achieves accurate social navigation with reduced computational energy expenditure
Data Source
AI summary
Systems and methods for game theoric path planning for social navigation are provided. In one embodiment, a method includes identifying a set of dynamic agents in an agent environment based on sensor data from one or more agent sensors of an ego agent. The method includes determining preference distributions for dynamic agent of the set of dynamic agents. The method includes determining a joint state for the dynamic agents of the set of dynamic agents by applying a recursive model to each dynamic agent to calculate a trajectory likelihood based on an expected interference risk of a candidate trajectory and the preference distributions. The joint state for the set of dynamic agents minimizes deviations from the goal state for each dynamic agent and minimizes the expected interference risk. The method includes causing the ego agent to execute a path plan based on the joint state for the dynamic agents.