Organizational Unit:
Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 40
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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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    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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    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.
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    Path Planning with Uncertainty: Voronoi Uncertainty Fields
    (Georgia Institute of Technology, 2013-05) Ok, Kyel ; Ansari, Sameer ; Gallagher, Billy ; Sica, William ; Dellaert, Frank ; Stilman, Mike
    In this paper, a two-level path planning algorithm that deals with map uncertainty is proposed. The higher level planner uses modified generalized Voronoi diagrams to guarantee finding a connected path from the start to the goal if a collision-free path exists. The lower level planner considers uncertainty of the observed obstacles in the environment and assigns repulsive forces based on their distance to the robot and their positional uncertainty. The attractive forces from the Voronoi nodes and the repulsive forces from the uncertainty- biased potential fields form a hybrid planner we call Voronoi Uncertainty Fields (VUF). The proposed planner has two strong properties: (1) bias against uncertain obstacles, and (2) completeness. We analytically prove the properties and run simulations to validate our method in a forest-like environment.
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    Planning with Movable Obstacles in Continuous Environments with Uncertain Dynamics
    (Georgia Institute of Technology, 2013-05) Levihn, Martin ; Scholz, Jonathan ; Stilman, Mike
    In 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.
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    Humanoid Robot Teleoperation for Tasks with Power Tools
    (Georgia Institute of Technology, 2013-04) O’Flaherty, Rowland ; Vieira, Peter ; Grey, M. X. ; Oh, Paul ; Bobick, Aaron F. ; Egerstedt, Magnus B. ; Stilman, Mike
    This paper presents the implementation of inverse kinematics to achieve teleoperation of a physical humanoid robot platform. The humanoid platform will be used to compete in the DARPA Robot Challenge, which requires autonomous execution of various search and rescue tasks, such as cutting through walls, which is a very practical application to robotics. Using a closed-form kinematic solution and a basic feedback controller, our objective of executing simple tasks is realized via teleoperation. Joint limits and singularities are accounted for using the different cases in the kinematic solution; and a decision method is implemented to determine how to position the end-effector when the goal is outside the feasible workspace.
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    Multi-Process Control Software for HUBO2 Plus Robot
    (Georgia Institute of Technology, 2013-04) Grey, M .X. ; Dantam, Neil ; Lofaro, Daniel M. ; Bobick, Aaron F. ; Egerstedt, Magnus B. ; Oh, Paul ; Stilman, Mike
    Humanoid robots require greater software reliability than traditional mechantronic systems if they are to perform useful tasks in typical human-oriented environments. This paper covers a software architecture which distributes the load of computation and control tasks over multiple processes, enabling fail-safes within the software. These fail-safes ensure that unexpected crashes or latency do not produce damaging behavior in the robot. The distribution also offers benefits for future software development by making the architecture modular and extensible. Utilizing a low-latency inter-process communication protocol (Ach), processes are able to communicate with high control frequencies. The key motivation of this software architecture is to provide a practical framework for safe and reliable humanoid robot software development. The authors test and verify this framework on a HUBO2 Plus humanoid robot.
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    Detecting Partially Occluded Objects via Segmentation and Validation
    (Georgia Institute of Technology, 2013-01) Levihn, Martin ; Dutton, Matthew ; Trevor, Alexander J. B. ; Stilman, Mike
    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 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.