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Humanoid Robotics Laboratory

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Author: Nishiwaki, Koichi ×

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Autonomous Environment Manipulation to Assist Humanoid Locomotion

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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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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Humanoid Teleoperation for Whole Body Manipulation

2008-05 , Stilman, Mike , Nishiwaki, Koichi , Kagami, Satoshi

We present results of successful telemanipulation of large, heavy objects by a humanoid robot. Using a single joystick the operator controls walking and whole body manipulation along arbitrary paths for up to ten minutes of continuous execution. The robot grasps, walks, pushes, pulls, turns and re-grasps a 55kg range of loads on casters. Our telemanipulation framework changes reference frames online to let the operator steer the robot in free walking, its hands in grasping and the object during mobile manipulation. In the case of manipulation, our system computes a robot motion that satisfies the commanded object path as well as the kinematic and dynamic constraints of the robot. Furthermore, we achieve increased robot stability by learning dynamic friction models of manipulated objects.

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Learning Object Models for Humanoid Manipulation

2007-11 , Stilman, Mike , Nishiwaki, Koichi , Kagami, Satoshi

We present a successful implementation of rigid grasp manipulation for large objects moved along specified trajectories by a humanoid robot. HRP-2 manipulates tables on casters with a range of loads up to its own mass. The robot maintains dynamic balance by controlling its center of gravity to compensate for reflected forces. To achieve high performance for large objects with unspecified dynamics the robot learns a friction model for each object and applies it to torso trajectory generation. We empirically compare this method to a purely reactive strategy and show a significant increase in predictive power and stability.

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