Organizational Unit:
Humanoid Robotics Laboratory

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Whole-Body Trajectory Optimization for Humanoid Falling

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.

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Robot Jenga: Autonomous and Strategic Block Extraction

2009 , Wang, Jiuguang , Rogers, Philip , Parker, Lonnie T. , Brooks, Douglas Antwonne , Stilman, Mike

This paper describes our successful implementation of a robot that autonomously and strategically removes multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that achieves repeatable performance in this challenging physical domain. In contrast to previous implementations, we rely only on low-cost, readily available system components and use strategic algorithms to resolve system uncertainty. We present a three-stage planner for block extraction which considers block selection, extraction order, and physics-based simulation that evaluates removability. Existing vision techniques are combined in a novel sequence for the identification and tracking of blocks within the tower. Discussion of our approach is presented following experimental results on a 5-DOF robot manipulator.

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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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Optimized Control Strategies for Wheeled Humanoids and Mobile Manipulators

2009-12 , Stilman, Mike , Wang, Jiuguang , Teeyapan, Kasemsit , Marceau, Ray

Optimizing the control of articulated mobile robots leads to emergent behaviors that improve the effectiveness, efficiency and stability of wheeled humanoids and dynamically stable mobile manipulators. Our simulated results show that optimization over the target pose, height and control parameters results in effective strategies for standing, acceleration and deceleration. These strategies improve system performance by orders of magnitude over existing controllers. This paper presents a simple controller for robot motion and an optimization method for choosing its parameters. By using whole-body articulation, we achieve new skills such as standing and unprecedented levels of performance for acceleration and deceleration of the robot base. We describe a new control architecture, present a method for optimization, and illustrate its functionality through two distinct methods of simulation.

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