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 36

Robust and Efficient Communication for Real-Time Multi-Process Robot Software

(Georgia Institute of Technology, 2012-11) Dantam, Neil ; Stilman, Mike

We present a new Interprocess Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating drivers, controllers, and algorithms in complex robotic systems such as humanoid robots. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems, and we discuss the implementation on our humanoid robot Golem Krang. Finally, the source code for Ach is available under an Open Source permissive license.
Deterministic Motion Planning for Redundant Robots along End-Effector Paths

(Georgia Institute of Technology, 2012-11) Quispe, Ana Huamán ; Stilman, Mike

In this paper we propose a deterministic approach to solve the Motion Planning along End-Effector Paths problem (MPEP) for redundant manipulators. Most of the existing approaches are based on local optimization techniques, hence they do not offer global guarantees of finding a path if it exists. Our proposed method is resolution complete. This feature is achieved by discretizing the Jacobian nullspace at each waypoint and selecting the next configuration according to a given heuristic function. To escape from possible local minima, our algorithm implements a backtracking strategy that allows our planner to recover from erroneous previous configuration choices by performing a breadth-first backwards search procedure. We present the results of simulated experiments performed with diverse manipulators and a humanoid robot.
Linguistic Transfer of Human Assembly Tasks to Robots

(Georgia Institute of Technology, 2012-10) Dantam, Neil ; Essa, Irfan ; Stilman, Mike

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.
Multi-Robot Multi-Object Rearrangement in Assignment Space

(Georgia Institute of Technology, 2012-10) Levihn, Martin ; Igarashi, Takeo ; Stilman, Mike

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.
Manipulation Planning with Soft Task Constraints

(Georgia Institute of Technology, 2012-10) Kunz, Tobias ; Stilman, Mike

We present a randomized configuration space planner that enforces soft workspace task constraints. A soft task constraint allows an interval of feasible values while favoring a given exact value. Previous work only allows for enforcing an exact value or an interval without a specific preference. Soft task constraints are a useful concept in everyday life. For example when carrying a container of liquid we want to keep it as close to the upright position as possible but want to be able to tilt it slightly in order to avoid obstacles. This paper introduces the necessary algorithms for handling such constraints, including projection methods and useful representations of everyday constraints. Our algorithms are evaluated on a series of simulated benchmark problems and shown to yield significant improvement in constraint satisfaction.
Diverse Workspace Path Planning for Robot Manipulators

(Georgia Institute of Technology, 2012-07) Quispe, Ana Huamán ; Stilman, Mike

We present a novel algorithm that generates a set of diverse workspace paths for manipulators. By considering more than one possible path we give our manipulator the flexibility to choose from many possible ways to execute a task. This is particularly important in cases in which the best workspace path cannot be executed by the manipulator (e.g. due to the presence of obstacles that collide with the manipulator links). Our workspace paths are generated such that a distance metric between them is maximized, allowing them to span different workspace regions. Manipulator planners mostly focus on solving the problem by analyzing the configuration space (e.g. Jacobian-based methods); our approach focuses on analyzing alternative workspace paths which are comparable to the optimal solution in terms of length. This paper introduces our intuitive algorithm and also presents the results of a series of experiments performed with a simulated 7 DOF robotic arm.
Time-Optimal Trajectory Generation for Path Following with Bounded Acceleration and Velocity

(Georgia Institute of Technology, 2012-07) Kunz, Tobias ; Stilman, Mike

This paper presents a novel method to generate the time-optimal trajectory that exactly follows a given differentiable joint-space path within given bounds on joint accelerations and velocities. We also present a path preprocessing method to make nondifferentiable paths differentiable by adding circular blends. We introduce improvements to existing work that make the algorithm more robust in the presence of numerical inaccuracies. Furthermore we validate our methods on hundreds of randomly generated test cases on simulated and real 7-DOF robot arms. Finally, we provide open source software that implements our algorithms.
Whole-Body Trajectory Optimization for Humanoid Falling

(Georgia Institute of Technology, 2012-06) Wang, Jiuguang ; Whitman, Eric C. ; Stilman, Mike

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.
Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles

(Georgia Institute of Technology, 2012-06) Levihn, Martin ; Scholz, Jonathan ; Stilman, Mike

In this paper we present the first decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO). While efficient planners for NAMO exist, they are challenging to implement in practice due to the inherent uncertainty in both perception and control of real robots. Generalizing existing NAMO planners to nondeterministic domains is particularly difficult due to the sensitivity of MDP methods to task dimensionality. Our work addresses this challenge by combining ideas from Hierarchical Reinforcement Learning with Monte Carlo Tree Search, and results in an algorithm that can be used for fast online planning in uncertain environments. We evaluate our algorithm in simulation, and provide a theoretical argument for our results which suggest linear time complexity in the number of obstacles for typical environments.
The Motion Grammar Calculus for Context-Free Hybrid Systems

(Georgia Institute of Technology, 2012-06) Dantam, Neil ; Stilman, Mike

This paper provides a method for deriving provably correct controllers for Hybrid Dynamical Systems with Context-Free discrete dynamics, nonlinear continuous dynamics, and nonlinear state partitioning. The proposed method models the system using a Context-Free Motion Grammar and specifies correct performance using a Regular language representation such as Linear Temporal Logic. The initial model is progressively rewritten via a calculus of symbolic transformation rules until it satisfies the desired specification.