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Humanoid Robotics Laboratory

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The Motion Grammar: Analysis of a Linguistic Method for Robot Control

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 and Executing Navigation Among Movable Obstacles

2007 , Stilman, Mike , Nishiwaki, Koichi , Kagami, Satoshi , Kuffner, James J.

This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain of Navigation Among Movable Obstacles (NAMO). The robot perceives and constructs a model of an environment filled with various fixed and movable obstacles, and automatically plans a navigation strategy to reach a desired goal location. The planned strategy consists of a sequence of walking and compliant manipulation operations. It is executed by the robot with online feedback. We give an overview of our NAMO system, as well as provide details of the autonomous planning, online grasping and compliant hand positioning during dynamically-stable walking. Finally, we present results of a successful implementation running on the Humanoid Robot HRP-2.

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Global Manipulation Planning in Robot Joint Space With Task Constraints

2010-06 , Stilman, Mike

We explore global randomized joint space path planning for articulated robots that are subject to task space constraints. This paper describes a representation of constrained motion for joint space planners and develops two simple and efficient methods for constrained sampling of joint configurations: Tangent Space Sampling (TS) and First-Order Retraction (FR). FR is formally proven to provide global sampling for linear task space transformations. Constrained joint space planning is important for many real world problems involving redundant manipulators. On the one hand, tasks are designated in work space coordinates: rotating doors about fixed axes, sliding drawers along fixed trajectories or holding objects level during transport. On the other, joint space planning gives alternative paths that use redundant degrees of freedom to avoid obstacles or satisfy additional goals while performing a task. We demonstrate that our methods are faster and more invariant to parameter choices than existing techniques.

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Navigation Among Movable Obstacles: Real-Time Reasoning in Complex Environments

2005-12 , Stilman, Mike , Kuffner, James J.

In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multi-object domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.

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Planning Among Movable Obstacles with Artificial Constraints

2008-11 , Stilman, Mike , Kuffner, James J.

This paper presents artificial constraints as a method for guiding heuristic search in the computationally challenging domain of motion planning among movable obstacles. The robot is permitted to manipulate unspecified obstacles in order to create space for a path. A plan is an ordered sequence of paths for robot motion and object manipulation. We show that under monotone assumptions, anticipating future manipulation paths results in constraints on both the choice of objects and their placements at earlier stages in the plan. We present an algorithm that uses this observation to incrementally reduce the search space and quickly find solutions to previously unsolved classes of movable obstacle problems. Our planner is developed for arbitrary robot geometry and kinematics. It is presented with an implementation for the domain of navigation among movable obstacles.

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