Humanoid Robotics Laboratory
Humanoid Robotics Laboratory
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ItemMulti-Robot Multi-Object Rearrangement in Assignment Space(Georgia Institute of Technology, 2012-10) Levihn, Martin ; Igarashi, Takeo ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines ; Georgia Institute of Technology. College of Computing ; University of TokyoWe present Assignment Space Planning, a new efficient robot multi-agent coordination algorithm for the PSPACE- hard problem of multi-robot multi-object push rearrangement. In both simulated and real robot experiments, we demonstrate that our method produces optimal solutions for simple problems and exhibits novel emergent behaviors for complex scenarios. Assignment Space takes advantage of the domain structure by splitting the planning up into three stages, effectively reducing the search space size and enabling the planner to produce optimized plans in seconds. Our algorithm finds solutions of comparable quality to complete configuration space search while reducing the computing time to seconds, which allows our approach to be applied in practical scenarios in real-time.
ItemPlanning with Movable Obstacles in Continuous Environments with Uncertain Dynamics(Georgia Institute of Technology, 2013-05) Levihn, Martin ; Scholz, Jonathan ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent MachinesIn this paper we present a decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO) operating under conditions faced by real robotic systems. While planners for the NAMO domain exist, they typically assume a deterministic environment or rely on discretization of the configuration and action spaces, preventing their use in practice. In contrast, we propose a planner that operates in real-world conditions such as uncertainty about the parameters of workspace objects and continuous configuration and action (control) spaces. To achieve robust NAMO planning despite these conditions, we introduce a novel integration of Monte Carlo simulation with an abstract MDP construction. We present theoretical and empirical arguments for time complexity linear in the number of obstacles as well as a detailed implementation and examples from a dynamic simulation environment.
ItemDetecting Partially Occluded Objects via Segmentation and Validation(Georgia Institute of Technology, 2013-01) Levihn, Martin ; Dutton, Matthew ; Trevor, Alexander J. B. ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent MachinesThis paper presents a novel algorithm: Verfied Partial Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds. VPOD is implemented and validated on real sensor data obtained by our robot. It extends Viewpoint Feature His- tograms (VFH), which classify unoccluded objects, to also classify partially occluded objects such as furniture that might be seen in typical office environments. To achieve this result, VPOD employs two strategies. First, object models are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the complete object model is aligned back into the scene and verified for consistency with the point cloud data. Overall, our approach increases the number of objects found and substantially reduces false positives due to the verification process.