Organizational Unit:
Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 64
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    Towards Stable Balancing
    (Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Stilman, Mike
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    Autonomous Environment Manipulation to Assist Humanoid Locomotion
    (Georgia Institute of Technology, 2014) Levihn, Martin ; Nishiwaki, Koichi ; Kagami, Satoshi ; Stilman, Mike
    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.
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    Krang: Center of Mass Estimation
    (Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Volle, Kyle ; Stilman, Mike
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    Krang Kinematics: A Denavit-Hartenberg Parameterization
    (Georgia Institute of Technology, 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
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    Gravity and Drift in Force/Torque Measurements
    ( 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
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    Foresight and Reconsideration in Hierarchical Planning and Execution
    (Georgia Institute of Technology, 2013-11) Levihn, Martin ; Kaelbling, Leslie Pack ; Lozano-Pérez, Tomás ; Stilman, Mike
    We present a hierarchical planning and execution architecture that maintains the computational efficiency of hierar- chical decomposition while improving optimality. It provides mech- anisms for monitoring the belief state during execution and per- forming selective replanning to repair poor choices and take advan- tage of new opportunities. It also provides mechanisms for looking ahead into future plans to avoid making short-sighted choices. The effectiveness of this architecture is shown through comparative experiments in simulation and demonstrated on a real PR2 robot.
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    Probabilistic Human Action Prediction and Wait-sensitive Planning for Responsive Human-robot Collaboration
    (Georgia Institute of Technology, 2013-10) Hawkins, Kelsey P. ; Vo, Nam ; Bansal, Shray ; Bobic, Aaron F.
    A novel representation for the human component of multi-step, human-robot collaborative activity is presented. The goal of the system is to predict in a probabilistic manner when the human will perform different subtasks that may require robot assistance. The representation is a graphical model where the start and end of each subtask is explicitly represented as a probabilistic variable conditioned upon prior intervals. This formulation allows the inclusion of uncertain perceptual detections as evidence to drive the predictions. Next, given a cost function that describes the penalty for different wait times, we develop a planning algorithm which selects robot-actions that minimize the expected cost based upon the distribution over predicted human-action timings. We demonstrate the approach in assembly tasks where the robot must provide the right part at the right time depending upon the choices made by the human operator during the assembly.
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    Correct Software Synthesis for Stable Speed-Controlled Robotic Walking
    (Georgia Institute of Technology, 2013-06) Dantam, Neil ; Hereid, Ayonga ; Ames, Aaron ; Stilman, Mike
    We present a software synthesis method for speed- controlled robot walking based on supervisory control of a context-free Motion Grammar. First, we use Human-Inspired control to identify parameters for fixed speed walking and for transitions between fixed speeds, guaranteeing dynamic stability. Next, we build a Motion Grammar representing the discrete- time control for this set of speeds. Then, we synthesize C code from this grammar and generate supervisors¹ online to achieve desired walking speeds, guaranteeing correctness of discrete computation. Finally, we demonstrate this approach on the Aldebaran NAO, showing stable walking transitions with dynamically selected speeds.
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    The Motion Grammar: Analysis of a Linguistic Method for Robot Control
    (Georgia Institute of Technology, 2013-06) Dantam, Neil ; Stilman, Mike
    We present the Motion Grammar: an approach to represent and verify robot control policies based on Context-Free Grammars. The production rules of the grammar represent a top-down task decomposition of robot behavior. The terminal symbols of this language represent sensor readings that are parsed in real-time. Efficient algorithms for context-free parsing guarantee that online parsing is computationally tractable. We analyze verification properties and language constraints of this linguistic modeling approach, show a linguistic basis that unifies several existing methods, and demonstrate effectiveness through experiments on a 14-DOF manipulator interacting with 32 objects (chess pieces) and an unpredictable human adversary. We provide many of the algorithms discussed as Open Source, permissively licensed software. ¹
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    Planning in Constraint Space: Automated Design of Functional Structures
    (Georgia Institute of Technology, 2013-05) Erdogan, Can ; Stilman, Mike
    On the path to full autonomy, robotic agents have to learn how to manipulate their environments for their benefit. In particular, the ability to design structures that are functional in overcoming challenges is imperative. The problem of automated design of functional structures (ADFS) addresses the question of whether the objects in the environment can be placed in a useful configuration. In this work, we first make the observation that the ADFS problem represents a class of problems in high dimensional, continuous spaces that can be broken down into simpler subproblems with semantically meaningful actions. Next, we propose a framework where discrete actions that induce constraints can partition the solution space effectively. Subsequently, we solve the original class of problems by searching over the available actions, where the evaluation criteria for the search is the feasibility test of the accumulated constraints. We prove that with a sound feasibility test, our algorithm is complete. Additionally, we argue that a convexity requirement on the constraints leads to significant efficiency gains. Finally, we present successful results to the ADFS problem.