Erion Plaku

Mohamed Aghzal, Erion Plaku, Ziyu Yao

Large language models (LLMs) have achieved remarkable success across a wide spectrum of tasks; however, they still face limitations in scenarios that demand long-term planning and spatial reasoning. To facilitate this line of research, in this work, we propose a new benchmark, termed $\textbf{P}$ath $\textbf{P}$lanning from $\textbf{N}$atural $\textbf{L}$anguage ($\textbf{PPNL}$). Our benchmark evaluates LLMs' spatial-temporal reasoning by formulating ''path planning'' tasks that require an LLM to navigate to target locations while avoiding obstacles and adhering to constraints. Leveraging this benchmark, we systematically investigate LLMs including GPT-4 via different few-shot prompting methodologies as well as BART and T5 of various sizes via fine-tuning. Our experimental results show the promise of few-shot GPT-4 in spatial reasoning, when it is prompted to reason and act interleavedly, although it still fails to perform long-term temporal reasoning. In contrast, while fine-tuned LLMs achieved impressive results on in-distribution reasoning tasks, they struggled to generalize to larger environments or environments with more obstacles.

Kevin Molloy, Erion Plaku, Amarda Shehu

Motivation: Due to the central role of protein structure in molecular recognition, great computational efforts are devoted to modeling protein structures and motions that mediate structural rearrangements. The size, dimensionality, and non-linearity of the protein structure space present outstanding challenges. Such challenges also arise in robot motion planning, and robotics-inspired treatments of protein structure and motion are increasingly showing high exploration capability. Encouraged by such findings, we debut here ROMEO, which stands for Robotics prOtein Motion ExplOration framework. ROMEO is an open-source, object-oriented platform that allows researchers access to and reproducibility of published robotics-inspired algorithms for modeling protein structures and motions, as well as facilitates novel algorithmic design via its plug-and-play architecture. Availability and implementation: ROMEO is written in C++ and is available in GitLab (https://github.com/). This software is freely available under the Creative Commons license (Attribution and Non-Commercial). Contact: amarda@gmu.edu

Mohamed Aghzal, Erion Plaku, Gregory J. Stein et al.

The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some researchers emphasize the potential of LLMs to perform complex planning tasks, others highlight significant limitations in their performance, particularly when these models are tasked with handling the intricacies of long-horizon reasoning. In this survey, we critically investigate existing research on the use of LLMs in automated planning, examining both their successes and shortcomings in detail. We illustrate that although LLMs are not well-suited to serve as standalone planners because of these limitations, they nonetheless present an enormous opportunity to enhance planning applications when combined with other approaches. Thus, we advocate for a balanced methodology that leverages the inherent flexibility and generalized knowledge of LLMs alongside the rigor and cost-effectiveness of traditional planning methods.

Mohamed Aghzal, Xiang Yue, Erion Plaku et al.

Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work, we generalize this question to consider LLMs augmented with visual understanding, i.e., Vision-Language Models (VLMs). We introduce PathEval, a novel benchmark evaluating VLMs as plan evaluators in complex path-planning scenarios. Succeeding in the benchmark requires a VLM to be able to abstract traits of optimal paths from the scenario description, demonstrate precise low-level perception on each path, and integrate this information to decide the better path. Our analysis of state-of-the-art VLMs reveals that these models face significant challenges on the benchmark. We observe that the VLMs can precisely abstract given scenarios to identify the desired traits and exhibit mixed performance in integrating the provided information. Yet, their vision component presents a critical bottleneck, with models struggling to perceive low-level details about a path. Our experimental results show that this issue cannot be trivially addressed via end-to-end fine-tuning; rather, task-specific discriminative adaptation of these vision encoders is needed for these VLMs to become effective path evaluators.

Hoang-Dung Bui, Erion Plaku, Gregoy J. Stein

This paper proposes a novel planning framework to handle a multi-agent pathfinding problem under team-connected communication constraint, where all agents must have a connected communication channel to the rest of the team during their entire movements. Standard multi-agent path finding approaches (e.g., priority-based search) have potential in this domain but fail when neighboring configurations at start and goal differ. Their single-expansion approach -- computing each agent's path from the start to the goal in just a single expansion -- cannot reliably handle planning under communication constraints for agents as their neighbors change during navigating. Similarly, leader-follower approaches (e.g., platooning) are effective at maintaining team communication, but fixing the leader at the outset of planning can cause planning to become stuck in dense-clutter environments, limiting their practical utility. To overcome this limitation, we propose a novel two-level multi-agent pathfinding framework that integrates two techniques: adaptive path expansion to expand agent paths to their goals in multiple stages; and dynamic leading technique that enables the reselection of the leading agent during each agent path expansion whenever progress cannot be made. Simulation experiments show the efficiency of our planners, which can handle up to 25 agents across five environment types under a limited communication range constraint and up to 11-12 agents on three environment types under line-of-sight communication constraint, exceeding 90% success-rate where baselines routinely fail.

Mohamed Aghzal, Erion Plaku, Ziyu Yao

Large Language Models (LLMs) have shown impressive capabilities across a wide variety of tasks. However, they still face challenges with long-horizon planning. To study this, we propose path planning tasks as a platform to evaluate LLMs' ability to navigate long trajectories under geometric constraints. Our proposed benchmark systematically tests path-planning skills in complex settings. Using this, we examined GPT-4's planning abilities using various task representations and prompting approaches. We found that framing prompts as Python code and decomposing long trajectory tasks improve GPT-4's path planning effectiveness. However, while these approaches show some promise toward improving the planning ability of the model, they do not obtain optimal paths and fail at generalizing over extended horizons.