48 research outputs found
Inferring Occluded Agent Behavior in Dynamic Games with Noise-Corrupted Observations
Robots and autonomous vehicles must rely on sensor observations, e.g., from
lidars and cameras, to comprehend their environment and provide safe, efficient
services. In multi-agent scenarios, they must additionally account for other
agents' intrinsic motivations, which ultimately determine the observed and
future behaviors. Dynamic game theory provides a theoretical framework for
modeling the behavior of agents with different objectives who interact with
each other over time. Previous works employing dynamic game theory often
overlook occluded agents, which can lead to risky navigation decisions. To
tackle this issue, this paper presents an inverse dynamic game technique which
optimizes the game model itself to infer unobserved, occluded agents' behavior
that best explains the observations of visible agents. Our framework
concurrently predicts agents' future behavior based on the reconstructed game
model. Furthermore, we introduce and apply a novel receding horizon planning
pipeline in several simulated scenarios. Results demonstrate that our approach
offers 1) robust estimation of agents' objectives and 2) precise trajectory
predictions for both visible and occluded agents from observations of only
visible agents. Experimental findings also indicate that our planning pipeline
leads to safer navigation decisions compared to existing baseline methods
Leadership Inference for Multi-Agent Interactions
Effectively predicting intent and behavior requires inferring leadership in
multi-agent interactions. Dynamic games provide an expressive theoretical
framework for modeling these interactions. Employing this framework, we propose
a novel method to infer the leader in a two-agent game by observing the agents'
behavior in complex, long-horizon interactions. We make two contributions.
First, we introduce an iterative algorithm that solves dynamic two-agent
Stackelberg games with nonlinear dynamics and nonquadratic costs, and
demonstrate that it consistently converges. Second, we propose the Stackelberg
Leadership Filter (SLF), an online method for identifying the leading agent in
interactive scenarios based on observations of the game interactions. We
validate the leadership filter's efficacy on simulated driving scenarios to
demonstrate that the SLF can draw conclusions about leadership that match
right-of-way expectations.Comment: 8 pages, 5 figures, submitted for publication to IEEE Robotics and
Automation Letter
Game-theoretic Occlusion-Aware Motion Planning: an Efficient Hybrid-Information Approach
We present a novel algorithm for motion planning in complex, multi-agent
scenarios in which occlusions prevent all agents from seeing one another. In
this setting, the fundamental information that each agent has, i.e., the
information structure of the interaction, is determined by the precise
configurations in which agents come into view of one another. Occlusions
prevent the use of existing pure feedback solutions, which assume availability
of the state information of all agents at every time step. On the other hand,
existing open-loop solutions only assume availability of the initial agent
states. Thus, they do not fully utilize the information available to agents
during periods of unhampered visibility. Here, we first introduce an algorithm
for solving an occluded, linear-quadratic (LQ) dynamic game, which computes
Nash equilibrium by using hybrid information and switching between feedback and
open-loop information structures. We then design an efficient iterative
algorithm for decision-making which exploits this hybrid information structure.
Our method is demonstrated in overtaking and intersection traffic scenarios.
Results confirm that our method outputs trajectories with favorable running
times, converging much faster than recent methods employing reachability
analysis
Robust Forecasting for Robotic Control: A Game-Theoretic Approach
Modern robots require accurate forecasts to make optimal decisions in the
real world. For example, self-driving cars need an accurate forecast of other
agents' future actions to plan safe trajectories. Current methods rely heavily
on historical time series to accurately predict the future. However, relying
entirely on the observed history is problematic since it could be corrupted by
noise, have outliers, or not completely represent all possible outcomes. To
solve this problem, we propose a novel framework for generating robust
forecasts for robotic control. In order to model real-world factors affecting
future forecasts, we introduce the notion of an adversary, which perturbs
observed historical time series to increase a robot's ultimate control cost.
Specifically, we model this interaction as a zero-sum two-player game between a
robot's forecaster and this hypothetical adversary. We show that our proposed
game may be solved to a local Nash equilibrium using gradient-based
optimization techniques. Furthermore, we show that a forecaster trained with
our method performs 30.14% better on out-of-distribution real-world lane change
data than baselines
Alternating Direction Method of Multipliers for Decomposable Saddle-Point Problems
Saddle-point problems appear in various settings including machine learning,
zero-sum stochastic games, and regression problems. We consider decomposable
saddle-point problems and study an extension of the alternating direction
method of multipliers to such saddle-point problems. Instead of solving the
original saddle-point problem directly, this algorithm solves smaller
saddle-point problems by exploiting the decomposable structure. We show the
convergence of this algorithm for convex-concave saddle-point problems under a
mild assumption. We also provide a sufficient condition for which the
assumption holds. We demonstrate the convergence properties of the saddle-point
alternating direction method of multipliers with numerical examples on a power
allocation problem in communication channels and a network routing problem with
adversarial costs.Comment: Accepted to 58th Annual Allerton Conference on Communication,
Control, and Computin