Problem
Research questions and friction points this paper is trying to address.
multi-agent navigation
environment optimization
safety
co-optimization
differentiable optimization
Differentiable Environment-Trajectory Co-Optimization for Safe Multi-Agent Navigation
🤖 AI Summary
This work addresses the limitation of conventional multi-agent navigation methods, which typically assume a static environment and overlook the potential of environmental configuration to enhance safety and efficiency. The authors propose a differentiable co-optimization framework that jointly optimizes environment parameters and agent trajectories through a bilevel optimization formulation: the lower level employs an interior-point method to minimize trajectory costs for agents, while the upper level adjusts environmental parameters via gradient ascent to improve navigation safety. A novel safety metric grounded in measure theory is introduced, and end-to-end gradient propagation is enabled by leveraging KKT conditions and the implicit function theorem. Experimental results demonstrate that the proposed approach significantly enhances both safety and efficiency of multi-agent navigation in warehouse logistics and urban traffic scenarios.
📝 Abstract
The environment plays a critical role in multi-agent navigation by imposing spatial constraints, rules, and limitations that agents must navigate around. Traditional approaches treat the environment as fixed, without exploring its impact on agents' performance. This work considers environment configurations as decision variables, alongside agent actions, to jointly achieve safe navigation. We formulate a bi-level problem, where the lower-level sub-problem optimizes agent trajectories that minimize navigation cost and the upper-level sub-problem optimizes environment configurations that maximize navigation safety. We develop a differentiable optimization method that iteratively solves the lower-level sub-problem with interior point methods and the upper-level sub-problem with gradient ascent. A key challenge lies in analytically coupling these two levels. We address this by leveraging KKT conditions and the Implicit Function Theorem to compute gradients of agent trajectories w.r.t. environment parameters, enabling differentiation throughout the bi-level structure. Moreover, we propose a novel metric that quantifies navigation safety as a criterion for the upper-level environment optimization, and prove its validity through measure theory. Our experiments validate the effectiveness of the proposed framework in a variety of safety-critical navigation scenarios, inspired from warehouse logistics to urban transportation. The results demonstrate that optimized environments provide navigation guidance, improving both agents' safety and efficiency.
Innovation
Methods, ideas, or system contributions that make the work stand out.
differentiable bi-level optimization
environment-trajectory co-optimization
multi-agent navigation safety
KKT-based gradient computation
implicit function theorem
🔎 Similar Papers
No similar papers found.
