Vasilopoulos, Vasileios

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Now showing 1 - 8 of 8
  • Publication
    Sensor-Based Legged Robot Homing Using Range-Only Target Localization
    (2017-12-01) Vasilopoulos, Vasileios; Arslan, Omur; De, Avik; Koditschek, Daniel E
    This paper demonstrates a fully sensor-based reactive homing behavior on a physical quadrupedal robot, using onboard sensors, in simple (convex obstacle-cluttered) unknown, GPS-denied environments. Its implementation is enabled by our empirical success in controlling the legged machine to approximate the (abstract) unicycle mechanics assumed by the navigation algorithm, and our proposed method of range-only target localization using particle filters. For more information: Kod*lab
  • Publication
    Sensor-Based Reactive Symbolic Planning in Partially Known Environments
    (2018-05-01) Vasilopoulos, Vasileios; Vega-Brown, William; Arslan, Omur; Koditschek, Daniel E.; Roy, Nicholas
    This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption. A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration. The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner that requires little onboard computation and is used online. The validity of the proposed method is verified both with formal proofs and numerical simulations. For more information: Kod*lab
  • Publication
    Reactive Planning for Mobile Manipulation Tasks in Unexplored Semantic Environments
    (2021-05-01) Vasilopoulos, Vasileios; Kantaros, Yiannis; Pappas, George J.; Koditschek, Daniel E.
    Complex manipulation tasks, such as rearrangement planning of numerous objects, are combinatorially hard problems. Existing algorithms either do not scale well or assume a great deal of prior knowledge about the environment, and few offer any rigorous guarantees. In this paper, we propose a novel hybrid control architecture for achieving such tasks with mobile manipulators. On the discrete side, we enrich a temporal logic specification with mobile manipulation primitives such as moving to a point, and grasping or moving an object. Such specifications are translated to an automaton representation, which orchestrates the physical grounding of the task to mobility or manipulation controllers. The grounding from the discrete to the continuous reactive controller is online and can respond to the discovery of unknown obstacles or decide to push out of the way movable objects that prohibit task accomplishment. Despite the problem complexity, we prove that, under specific conditions, our architecture enjoys provable completeness on the discrete side, provable termination on the continuous side, and avoids all obstacles in the environment. Simulations illustrate the efficiency of our architecture that can handle tasks of increased complexity while also responding to unknown obstacles or unanticipated adverse configurations. For more information: Kod*lab
  • Publication
    Composition of Templates for Transitional Pedipulation Behaviors
    (2019-09-05) Topping, Thomas T; Vasilopoulos, Vasileios; De, Avik; Koditschek, Daniel E.
    Abstract. We document the reliably repeatable dynamical mounting and dismounting of wheeled stools and carts, and of fixed ledges, by the Minitaur robot. Because these tasks span a range of length scales that preclude quasi-static execution, we use a hybrid dynamical systems framework to variously compose and thereby systematically reuse a small lexicon of templates (low degree of freedom behavioral primitives). The resulting behaviors comprise the key competences beyond mere locomotion required for robust implementation on a legged mobile manipulator of a simple version of the warehouseman’s problem.
  • Publication
    Towards Bipedal Behavior on a Quadrupedal Platform Using Optimal Control
    (2016-05-13) Topping, Turner; Vasilopoulos, Vasileios; De, Avik; Koditschek, Daniel E
    This paper explores the applicability of a Linear Quadratic Regulator (LQR) controller design to the problem of bipedal stance on the Minitaur [1] quadrupedal robot. Restricted to the sagittal plane, this behavior exposes a three degree of freedom (DOF) double inverted pendulum with extensible length that can be projected onto the familiar underactuated revolute-revolute “Acrobot” model by assuming a locked prismatic DOF, and a pinned toe. While previous work has documented the successful use of local LQR control to stabilize a physical Acrobot, simulations reveal that a design very similar to those discussed in the past literature cannot achieve an empirically viable controller for our physical plant. Experiments with a series of increasingly close physical facsimiles leading to the actual Minitaur platform itself corroborate and underscore the physical Minitaur platform corroborate and underscore the implications of the simulation study. We conclude that local LQR-based linearized controller designs are too fragile to stabilize the physical Minitaur platform around its vertically erect equilibrium and end with a brief assessment of a variety of more sophisticated nonlinear control approaches whose pursuit is now in progress.
  • Publication
    Reactive Navigation in Partially Known Non-Convex Environments
    (2018-12-01) Vasilopoulos, Vasileios; Koditschek, Daniel E
    This paper presents a provably correct method for robot navigation in 2D environments cluttered with familiar but unexpected non-convex, star-shaped obstacles as well as completely unknown, convex obstacles. We presuppose a limited range onboard sensor, capable of recognizing, localizing and (leveraging ideas from constructive solid geometry) generating online from its catalogue of the familiar, non-convex shapes an implicit representation of each one. These representations underlie an online change of coordinates to a completely convex model planning space wherein a previously developed online construction yields a provably correct reactive controller that is pulled back to the physically sensed representation to generate the actual robot commands. We extend the construction to differential drive robots, and suggest the empirical utility of the proposed control architecture using both formal proofs and numerical simulations. For more information: Kod*lab
  • Publication
    Sensor-Based Reactive Execution of Symbolic Rearrangement Plans by a Legged Mobile Manipulator
    (2018-10-01) Vasilopoulos, Vasileios; Topping, T. Turner; Vega-Brown, William; Koditschek, Daniel E.; Roy, Nicholas
    We demonstrate the physical rearrangement of wheeled stools in a moderately cluttered indoor environment by a quadrupedal robot that autonomously achieves a user's desired configuration. The robot's behaviors are planned and executed by a three layer hierarchical architecture consisting of: an offline symbolic task and motion planner; a reactive layer that tracks the reference output of the deliberative layer and avoids unanticipated obstacles sensed online; and a gait layer that realizes the abstract unicycle commands from the reactive module through appropriately coordinated joint level torque feedback loops. This work also extends prior formal results about the reactive layer to a broad class of nonconvex obstacles. Our design is verified both by formal proofs as well as empirical demonstration of various assembly tasks. For more information: Kod*lab
  • Publication
    Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback
    (2020-06-01) Vasilopoulos, Vasileios; Pavlakos, Georgios; Bowman, Sean L.; Caporale, J. Diego; Daniilidis, Kostas; Pappas, George J.; Koditschek, Daniel E.
    This paper presents a reactive planning system that enriches the topological representation of an environment with a tightly integrated semantic representation, achieved by incorporating and exploiting advances in deep perceptual learning and probabilistic semantic reasoning. Our architecture combines object detection with semantic SLAM, affording robust, reactive logical as well as geometric planning in unexplored environments. Moreover, by incorporating a human mesh estimation algorithm, our system is capable of reacting and responding in real time to semantically labeled human motions and gestures. New formal results allow tracking of suitably non-adversarial moving targets, while maintaining the same collision avoidance guarantees. We suggest the empirical utility of the proposed control architecture with a numerical study including comparisons with a state-of-the-art dynamic replanning algorithm, and physical implementation on both a wheeled and legged platform in different settings with both geometric and semantic goals. For more information: Kod*lab