Browsing by Author "Chamzas, Constantinos"

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    MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
    (IEEE, 2022) Chamzas, Constantinos; Quintero-Peña, Carlos; Kingston, Zachary; Orthey, Andreas; Rakita, Daniel; Gleicher, Michael; Toussaint, Marc; Kavraki, Lydia E.
    Recently, there has been a wealth of development in motion planning for robotic manipulation—new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker , an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.
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    Retrieval-Based Learning for Efficient High-DoF Motion Planning
    (2023-04-20) Chamzas, Constantinos; Kavraki, Lydia E; Shrivastava, Anshumali
    Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, in the absence of any prior information, sampling-based planners are forced to explore uniformly or heuristically, which can lead to degraded performance. One way to improve performance is to use prior knowledge of environments to adapt the sampling strategy. In this thesis, we propose Pyre, a retrieval-based framework for learning sampling distributions for high-dimensional motion planning. Pyre decomposes the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and storing them in a database. Pyre synthesizes a global sampler by learning to retrieve local experiences relevant to the given situation. This method transfers knowledge between diverse environments that share local primitives and significantly speeds up planning. Pyre learns incrementally and significantly improves performance with only a handful of examples, achieving better generalization over diverse tasks and environments as compared to prior approaches. We demonstrate the effectiveness of Pyre on 2D and 3D environments as well as with a simulated and real Fetch Robot tasked with challenging pick-and-place manipulation problems. Additionally, in this thesis, we also contribute MotionBenchMaker, an open-source benchmarking tool for realistic robot manipulation problems. It is an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms.
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    Sampling-Based Motion Planning: A Comparative Review
    (Annual Reviews, 2024) Orthey, Andreas; Chamzas, Constantinos; Kavraki, Lydia E.
    Sampling-based motion planning is one of the fundamental paradigms to generate robot motions, and a cornerstone of robotics research. This comparative review provides an up-to-date guide and reference manual for the use of sampling-based motion planning algorithms. It includes a history of motion planning, an overview of the most successful planners, and a discussion of their properties. It also shows how planners can handle special cases and how extensions of motion planning can be accommodated. To put sampling-based motion planning into a larger context, a discussion of alternative motion generation frameworks highlights their respective differences from sampling-based motion planning. Finally, a set of sampling-based motion planners are compared on 24 challenging planning problems in order to provide insights into which planners perform well in which situations and where future research would be required. This comparative review thereby provides not only a useful reference manual for researchers in the field but also a guide for practitioners to make informed algorithmic decisions.