Organizational Unit:

Humanoid Robotics Laboratory

Permanent Link

https://hdl.handle.net/1853/70718

Parent Organization

Organizational Unit

Institute for Robotics and Intelligent Machines (IRIM)

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Publication Search Results

Now showing 1 - 10 of 64

Navigation Among Movable Obstacles in Unknown Environments

(Georgia Institute of Technology, 2010-10) Wu, Hai-Ning ; Levihn, Martin ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines

This paper explores the Navigation Among Movable Obstacles (NAMO) problem in an unknown environment. We consider the realistic scenario in which the robot has to navigate to a goal position in an unknown environment consisting of static and movable objects. The robot may move objects if the goal can not be reached otherwise or if moving the object may significantly shorten the path to the goal. We consider real situations in which the robot only has limited sensing information and where the action selection can therefore only be based on partial knowledge learned from the environment at that point. This paper introduces an algorithm that significantly reduces the necessary calculations to accomplish this task compared to a direct approach. We present an efficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios with more than 50 objects.
Autonomous Environment Manipulation to Assist Humanoid Locomotion

(Georgia Institute of Technology, 2014) Levihn, Martin ; Nishiwaki, Koichi ; Kagami, Satoshi ; Stilman, Mike ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; National Institute of Advanced Industrial Science and Technology (Japan). Digital Human Research Center

Legged robots have unique capabilities to traverse complex environments by stepping over and onto objects. Many footstep planners have been developed to take advantage of these capabilities. However, legged robots also have inherent constraints such as a maximum step height and distance. These constraints typically limit their reachable space, independent of footstep planning. Thus, we propose that robots such as humanoid robots that have manipulation capabilities should use them. A robot should autonomously modify its environment if necessary. We present a system that enabled a real robot to use a box to create itself a stair step or place a board on the ground to cross a gap, allowing it to reach its otherwise unreachable goal configuration.
Linguistic Transfer of Human Assembly Tasks to Robots

(Georgia Institute of Technology, 2012-10) Dantam, Neil ; Essa, Irfan ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines ; Georgia Institute of Technology. College of Computing

We demonstrate the automatic transfer of an assembly task from human to robot. This work extends efforts showing the utility of linguistic models in verifiable robot control policies by now performing real visual analysis of human demonstrations to automatically extract a policy for the task. This method tokenizes each human demonstration into a sequence of object connection symbols, then transforms the set of sequences from all demonstrations into an automaton, which represents the task-language for assembling a desired object. Finally, we combine this assembly automaton with a kinematic model of a robot arm to reproduce the demonstrated task.
Robot Path Planning Using Field Programmable Analog Arrays

(Georgia Institute of Technology, 2012-05) Koziol, Scott ; Hasler, Jennifer ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines ; Georgia Institute of Technology. School of Interactive Computing

We present the successful application of reconfigurable Analog-Very-Large-Scale-Integrated (AVLSI) circuits to motion planning for the AmigoBot robot. Previous research has shown that custom application-specific-integrated-circuits (ASICs) can be used for robot path planning. However, ASICs are typically fixed circuit designs that require long fabrication times on the order of months. In contrast, our reconfigurable analog circuits called Field Programmable Analog Arrays (FPAAs) implement a variety of AVLSI circuits in minutes. We present experimental results of online robot path planning using FPAA circuitry, validating our assertion that FPAA-based AVLSI design is a feasible approach to computing complete motion plans using analog floating-gate resistive grids. We demonstrate the integration of FPAA hardware and software with a real robot platform and hardware in the loop simulations, present the trajectories developed by our planner and provide analysis of the time and space complexity of our proposed approach. The paper concludes by formulating metrics that identify domains where analog solutions to planning may be faster and more efficient than traditional, digital robot planning techniques.
Krang Kinematics: A Denavit-Hartenberg Parameterization

(Georgia Institute of Technology, 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; Georgia Institute of Technology. College of Computing
Multi-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 Tokyo

We 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.
Push Planning for Object Placement on Cluttered Table Surfaces

(Georgia Institute of Technology, 2011-09) Cosgun, Akansel ; Hermans, Tucker ; Emeli, Victor ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines ; Georgia Institute of Technology. School of Interactive Computing

We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation.
Krang: Center of Mass Estimation

(Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Volle, Kyle ; Stilman, Mike ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; Georgia Institute of Technology. College of Computing
Whole-Body Trajectory Optimization for Humanoid Falling

(Georgia Institute of Technology, 2012-06) Wang, Jiuguang ; Whitman, Eric C. ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines ; Carnegie-Mellon University

We present an optimization-based control strategy for generating whole-body trajectories for humanoid robots in order to minimize damage due to falling. In this work, the falling problem is formulated using optimal control where we seek to minimize the impulse on impact with the ground, subject to the full-body dynamics and constraints of the robot in joint space. We extend previous work in this domain by numerically approximating the resulting optimal control, generating open-loop trajectories by solving an equivalent nonlinear programming problem. Compared to previous results in falling optimization, the proposed framework is extendable to more complex dynamic models and generate trajectories that are guaranteed to be physically feasible. These results are implemented in simulation using models of dynamically balancing humanoid robots in several experimental scenarios.
Detecting Partially Occluded Objects via Segmentation and Validation

(Georgia Institute of Technology, 2012) Levihn, Martin ; Dutton, Matthew ; Trevor, Alexander J. B. ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines

This 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 Histograms (VFH) which classify unoccluded objects to also classifying 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 full 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.