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Humanoid Robotics Laboratory

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Now showing 1 - 10 of 11
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Equations of Motion for Dynamically Stable Mobile Manipulators

2010-12-14 , Dantam, Neil , Kolhe, Pushkar , Stilman, Mike

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Real-Time Path Planning for a Robot Arm in Changing Environments

2010-10 , Kunz, Tobias , Reiser, Ulrich , Stilman, Mike , Verl, Alexander

We present a practical strategy for real-time path planning for articulated robot arms in changing environments by integrating PRM for Changing Environments with 3D sensor data. Our implementation on Care-O-Bot 3 identifies bottlenecks in the algorithm and introduces new methods that solve the overall task of detecting obstacles and planning a path around them in under 100 ms. A fast planner is necessary to enable the robot to react to quickly changing human environments. We have tested our implementation in real-world experiments where a human subject enters the manipulation area, is detected and safely avoided by the robot. This capability is critical for future applications in automation and service robotics where humans will work closely with robots to jointly perform tasks.

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Dynamic Pushing Strategies for Dynamically Stable Mobile Manipulators

2010-05 , Kolhe, Pushkar , Dantam, Neil , Stilman, Mike

This paper presents three effective manipulation strategies for wheeled, dynamically balancing robots with articulated links. By comparing these strategies through analysis, simulation and robot experiments, we show that contact placement and body posture have a significant impact on the robot's ability to accelerate and displace environment objects. Given object geometry and friction parameters we determine the most effective methods for utilizing wheel torque to perform non-prehensile manipulation.

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The Motion Grammar: Linguistic Perception, Planning, and Control

2010 , Dantam, Neil , Stilman, Mike

We present the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and hybrid control that provides a computationally tractable way to control robots in uncertain environments with guarantees on completeness and correctness. The grammar represents a policy for the task which is parsed in real-time based on perceptual input. Branches of the syntax tree form the levels of a hierarchical decomposition, and the individual robot sensor readings are given by tokens. We implement this approach in the interactive game of Yamakuzushi on a physical robot resulting in a system that repeatably competes with a human opponent in sustained game-play for matches up to six minutes.

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Combining Motion Planning and Optimization for Flexible Robot Manipulation

2010-12 , Scholz, Jonathan , Stilman, Mike

Robots that operate in natural human environments must be capable of handling uncertain dynamics and underspecified goals. Current solutions for robot motion planning are split between graph-search methods, such as RRT and PRM which offer solutions to high-dimensional problems, and Reinforcement Learning methods, which relieve the need to specify explicit goals and action dynamics. This paper addresses the gap between these methods by presenting a task-space probabilistic planner which solves general manipulation tasks posed as optimization criteria. Our approach is validated in simulation and on a 7-DOF robot arm that executes several tabletop manipulation tasks. First, this paper formalizes the problem of planning in underspecified domains. It then describes the algorithms necessary for applying this approach to planar manipulation tasks. Finally it validates the algorithms on a series of sample tasks that have distinct objectives, multiple objects with different shapes/dynamics, and even obstacles that interfere with object motion.

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Stable Stacking for the Distributor’s Pallet Packing Problem

2010-10 , Schuster, Martin , Bormann, Richard , Steidl, Daniela , Reynolds-Haertle, Saul , Stilman, Mike

We present a novel algorithm that solves the distributor's pallet packing problem. In contrast to existing algorithms, our method optimizes stack stability in addition to stack volume. Furthermore, our algorithm explicitly handles cases where the construction of homogeneous layers of packages with equal height is impossible due to differences in package heights and quantities. The algorithm is a nested beam search that separately optimizes local and global evaluation criteria. We show successful results on both real world and synthetic data sets, compare our performance to an existing algorithm and demonstrate experimental applications in simulation and on a real palletizing robot.

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Golem Krang: Dynamically Stable Humanoid Robot for Mobile Manipulation

2010-05 , Stilman, Mike , Olson, Jon , Gloss, William

What would humans be like if nature had invented the wheel? Golem Krang is a novel humanoid torso designed at Georgia Tech. The robot dynamically transforms from a .5 m static to a 1.5 m dynamic configuration. Our robot development has led to two advances in the design of platforms for mobility and manipulation: (1) A 2-DOF robot base that autonomously stands from horizontal rest; (2) A 4-DOF humanoid torso that adds a waist roll joint to replicate human torso folding and a yaw joint for spine rotation. The mobile torso also achieves autonomous standing in a constrained space while lifting a 40 kg payload. Golem validates our assertions by consistently achieving static-dynamic transformations. This paper describes the design of our mobile torso. It considers a number of factors including its suitability for human environments, mechanical simplicity and the ability to store potential and kinetic energy for handling heavy human and even super-human tasks.

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Navigation Among Movable Obstacles in Unknown Environments

2010-10 , Wu, Hai-Ning , Levihn, Martin , Stilman, Mike

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.

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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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Robot Limbo: Optimized Planning and Control for Dynamically Stable Robots Under Vertical Obstacles

2010-05 , Teeyapan, Kasemsit , Wang, Jiuguang , Kunz, Tobias , Stilman, Mike

We present successful control strategies for dynamically stable robots that avoid low ceilings and other vertical obstacles in a manner similar to limbo dances. Given the parameters of the mission, including the goal and obstacle dimensions, our method uses a sequential composition of IO-linearized controllers and applies stochastic optimization to automatically compute the best controller gains and references, as well as the times for switching between the different controllers. We demonstrate this system through numerical simulations, validation in a physics-based simulation environment, as well as on a novel two-wheeled platform. The results show that the generated control strategies are successful in mission planning for this challenging problem domain and offer significant advantages over hand-tuned alternatives.

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