Browsing by Author "Kingston, Zachary"
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Item 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.Item Robonaut 2 and you: Specifying and executing complex operations(IEEE, 2017) Baker, William; Kingston, Zachary; Moll, Mark; Badger, Julia; Kavraki, LydiaCrew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional platforms. This paper describes a system that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot developed by NASA to research co-robotic applications. The system includes a specification of constraints used to describe operations, and the supporting planning framework that solves constrained problems on R2 at interactive speeds. The paper is developed in reference to an illustrative, typical example of an operation R2 performs to highlight the challenges inherent to the problems R2 must face. Finally, the interface and planner is validated through a case-study using the guiding example on the physical robot in a simulated microgravity environment. This work reveals the complexity of employing humanoid caretaker robots and suggest solutions that are broadly applicable.Item Toward Efficient and General Multi-Modal Planning(2021-11-22) Kingston, Zachary; Kavraki, Lydia E.Complex robotic systems like mobile manipulators and humanoids will soon be employed outside the highly-structured domain of factories, entering cluttered kitchens, warehouses, and even space stations (e.g., NASA's Robonaut 2). For a robot to be effective in these domains, it is essential that the robot be able to manipulate and navigate the environment to achieve desired tasks, e.g., setting a table and fetching an object on the other side of the space station. These problems can be formulated as multi-modal planning problems, that is, there are a finite number of interactions the robot can have with the environment (e.g., picking up an object, grasping a handrail) each of which with a continuous infinity of possibilities (e.g., where the object is grasped or placed). In this work, we propose an efficient and general solver for these multi-modal problems by tackling three key issues. First, we address the challenges in efficiently solving the arbitrary manifold-constrained motion planning problems that arise in multi-modal planning with a framework that maintains theoretical guarantees of sampling-based planners. Second, we improve planner efficiency with an experience-based framework that learns from prior motion plans, leveraging the structure of multi-modal problems. Finally, we propose a guiding heuristic for multi-modal planners that generates a "lead" of specific actions to attempt, enabling efficient solutions to complex, long-horizon problems.