This paper describes a system that takes real-time user input to direct a robot swarm. The user interface is via drawing, and the user can create a single drawing or an animation to be represented by robots.
In this paper, we present a control framework for a quadrupedal robot that is capable of locomoting using several gaits
This paper investigates the effects of adding gestures to a physical game between a human and a humanoid robot.
This paper presents an automatic model reduction procedure for humanoid robots, which is task-specific.
In this paper, we demonstrate a successful application of an iterative learning control algorithm to automate the process of fine tuning choreographed human-speed motions on a 37 degree-of-freedom humanoid robot.
A number of foot placement strategies for walking have been proposed that make use of widely varying model complexities. Although a number of successful demonstrations have been individually shown in simulation and on physical robots, it is difficult to make a direct performance comparison due to the large differences in hardware, gait generation strategy, control system gains, actuator saturation limits, sensor noise, and many other physical limitations. Here we present a quantitative stability comparison of four foot-placement strategies.
This paper presents a method for synthesizing motions of a humanoid robot that receives an object from a human, with focus on a natural object passing scenario where the human initiates the passing motion by moving an object towards the robot, which continuously adapts its motion to the observed human motion in real time.
This paper discusses several general ray-shooting algorithms and their applications to these problems in robotics.
In this work, we outline a theoretical framework for whole body inertial parameter estimation, including the unactuated floating base. Using a least squares minimization approach, conducted within the nullspace of unmeasured degrees of freedom, we are able to use a partial force sensor set for full-body estimation, e.g. using only joint torque sensors, allowing for estimation when contact force measurement is unavailable or unreliable (e.g. due to slipping, rolling contacts, etc.).
In this paper, we demonstrate that a neuromuscular controller built based on the human anatomical structure and motion data can realize human-like responses to unexpected disturbances during locomotion.
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