-- Mixed Integer Linear Programs (MILPs) are essential tools for solving planning and scheduling problems across critical industries such as construction, manufacturing, and logistics. However, their widespread adoption is limited by long computational times, especially in large-scale, real-time scenarios. T o address this, we present a learning-based framework that leverages Behavior Cloning (BC) and Reinforcement Learning (RL) to train Graph Neural Networks (GNNs), producing high-quality initial solutions for warm-starting MILP solvers in Multi-Agent T ask Allocation and Scheduling Problems. Experimental results demonstrate that our method reduces optimization time and variance compared to traditional techniques while maintaining solution quality and feasibility. I. INTRODUCTION Mixed Integer Linear Programs (MILPs) serve as a fundamental framework for combinatorial optimization problems, facilitating solutions across a wide range of planning and scheduling tasks in logistics [1], construction [2] and manufacturing [3].

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, agile tasks such as playing table tennis. Modern techniques for learning from demonstration improve sample efficiency and scale to diverse data, but are rarely evaluated on agile tasks. In the case of reinforcement learning, achieving good performance requires training on high-fidelity simulators. To overcome these limitations, we develop a novel diffusion modeling approach that is offline, constraint-guided, and expressive of diverse agile behaviors. The key to our approach is a kinematic constraint gradient guidance (KCGG) technique that computes gradients through both the forward kinematics of the robot arm and the diffusion model to direct the sampling process. KCGG minimizes the cost of violating constraints while simultaneously keeping the sampled trajectory in-distribution of the training data. We demonstrate the effectiveness of our approach for time-critical robotic tasks by evaluating KCGG in two challenging domains: simulated air hockey and real table tennis. In simulated air hockey, we achieved a 25.4% increase in block rate, while in table tennis, we saw a 17.3% increase in success rate compared to imitation learning baselines.

The moment that tennis fans have been waiting for is almost finally here, with the Wimbledon Championships set to kick off next week. This year's tournament will see the likes of Petra Kvitova, Novak Djokovic and Carlos Alcaraz take to the grass. But in the near future, they could face stiff competition from an unlikely new contender - a robot. Scientists from Georgia Tech have developed a new robot named ESTHER (Experimental Sport Tennis Wheelchair Robot), which can zip around the court and even return human shots. The team believes the bot could serve as a training partner for professional players in the future, removing the psychological pressure of training against another human.

We study the scheduling of human-robot teams where the human and robotic agents share decision-making authority over scheduling decisions. Our goal is to design AI scheduling techniques that account for how people make decisions under different control schema.

Advanced robotic technology is opening up the possibility of integrating robots into the human workspace to improve productivity and decrease the strain of repetitive, arduous physical tasks currently performed by human workers. However, coordinating these teams is a challenging problem. We must understand how decision-making authority over scheduling decisions should be shared between team members and how the preferences of the team members should be included. We report the results of a human-subject experiment investigating how a robotic teammate should best incorporate the preferences of human teammates into the team's schedule. We find that humans would rather work with a robotic teammate that accounts for their preferences, but this desire might be mitigated if their preferences come at the expense of team efficiency.

Likert items and scales are often used in human subject studies to measure subjective responses of subjects to the treatment levels. In the field of human-robot interaction (HRI), with few widely accepted quantitative metrics, researchers often rely on Likert items and scales to evaluate their systems. However, there is a debate on what is the best statistical method to evaluate the differences between experimental treatments based on Likert item or scale responses. Likert responses are ordinal and not interval, meaning, the differences between consecutive responses to a Likert item are not equally spaced quantitatively. Hence, parametric tests like t-test, which require interval and normally distributed data, are often claimed to be statistically unsound in evaluating Likert response data. The statistical purist would use non-parametric tests, such as the Mann-Whitney U test, to evaluate the differences in ordinal datasets; however, non-parametric tests sacrifice the sensitivity in detecting differences a more conservative specificity -- or false positive rate. Finally, it is common practice in the field of HRI to sum up similar individual Likert items to form a Likert scale and use the t-test or ANOVA on the scale seeking the refuge of the central limit theorem. In this paper, we empirically evaluate the validity of the t-test vs. the Mann-Whitney U test for Likert items and scales. We conduct our investigation via Monte Carlo simulation to quantify sensitivity and specificity of the tests.

The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic joint perception-action reconnaissance environment in which a composite team of agents (e.g., robots) cooperate to fight dynamic, propagating firespots (e.g., targets). In FireCommander game, a team of agents must be tasked to optimally deal with a wildfire situation in an environment with propagating fire areas and some facilities such as houses, hospitals, power stations, etc. The team of agents can accomplish their mission by first sensing (e.g., estimating fire states), communicating the sensed fire-information among each other and then taking action to put the firespots out based on the sensed information (e.g., dropping water on estimated fire locations). The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming. There are four important facets of the FireCommander environment that overall, create a non-trivial game: (1) Complex Objectives: Multi-objective Stochastic Environment, (2)Probabilistic Environment: Agents' actions result in probabilistic performance, (3) Hidden Targets: Partially Observable Environment and, (4) Uni-task Robots: Perception-only and Action-only agents. The FireCommander environment is first-of-its-kind in terms of including Perception-only and Action-only agents for coordination. It is a general multi-purpose game that can be useful in a variety of combinatorial optimization problems and stochastic games, such as applications of Reinforcement Learning (RL), Learning from Demonstration (LfD) and Inverse RL (iRL).

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.