5 accepted papers
Spatiotemporal planning is critically important in fields like robotics, logistics, and naval operations, especially for problem specifications involving multiple constraints. Traditional approaches place the burden on end users to manually specify cost functions, constraints, or model parameters, a…
Advances in robot learning have enabled robots to generate skills for a variety of tasks. Yet, robot learning is typically sample inefficient, struggles to learn from data sources exhibiting varied behaviors, and does not naturally incorporate constraints. These properties are critical for fast, agi
Optimizing large-scale logistics is computationally challenging due to its scale and requirement to be robust to stochastic and time-varying weather disturbances. However, prior research in multi-agent reinforcement learning (MARL) does not address scenarios that capture complexity of logistics oper
Trajectory prediction and generation are crucial for autonomous robots in dynamic environments. While prior research has typically focused on either prediction or generation, our approach unifies these tasks to provide a versatile framework and achieve state-of-the-art performance. While diffusion m…
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work, we present Graph based Adversarial Modeling with Mutual Information (GrAMMI) for modeling the behavior of an adversaria…