Marcell Missura

Omnidirectional capture steps for bipedal walking

Robust walking on two legs has proven to be one of the most difficult challenges of humanoid robotics. Bipedal walkers are inherently unstable systems that are difficult to control due to the complexity of their full-body dynamics. Aside from the challenge of generating a walking motion itself, closed-loop algorithms are required to maintain the balance of the robot using foot placements and other disturbance-rejection strategies. In this work, we propose a hierarchical, omnidirectional gait control framework that is able to counteract strong perturbations using a combination of step-timing, foot-placement, and zero-moment-point strategies. The perturbations can occur from any direction at any time during the step. The controller will not only maintain balance, but also follow a given reference locomotion velocity while absorbing the disturbance. The calculation of the timing, the footstep locations, and the zero moment point is based on the linear inverted pendulum model and can be computed efficiently in closed form.

Lateral capture steps for bipedal walking

Bipedal walkers are difficult to control, inherently unstable systems. Besides the complexity of the walking motion itself, the balance of the robot constantly has to be maintained with good foot placements and other disturbance-rejection strategies. In this work, we are presenting a new, closed-loop control approach that addresses both, the problem of complexity and the challenge of maintaining balance during walking. We decouple walking motion from balance and combine them in a hierarchical framework allowing a foot placement-based balance regulator to control the timing and footstep coordinates of central pattern-generated stepping motions. Furthermore, we decompose the balance controller into three simple, independent modules that compute suitable estimates of timing and sagittal and lateral coordinates for the next footstep to maintain a nominal center of mass trajectory. We implemented the timing and the lateral step size components using the equations of a parameterized version of the linear inverted pendulum model that we fit to data collected from a walking robot. The parameter optimization has a significant impact on the accuracy of our predictions. We demonstrate the efficiency of our approach by performing experiments on a real biped. Results show that the robot is able to reliably recover from any lateral push in only a few steps as long as it does not tip over the current support leg.

Humanoid TeenSize Open Platform NimbRo-OP

In recent years, the introduction of affordable platforms in the KidSize class of the Humanoid League has had a positive impact on the performance of soccer robots. The lack of readily available larger robots, however, severely affects the number of participants in Teen- and AdultSize and consequently the progress of research that focuses on the challenges arising with robots of larger weight and size. This paper presents the first hardware release of a low cost Humanoid TeenSize open platform for research, the first software release, and the current state of ROS-based software development. The NimbRo-OP robot was designed to be easily manufactured, assembled, repaired, and modified. It is equipped with a wide-angle camera, ample computing power, and enough torque to enable full-body motions, such as dynamic bipedal locomotion, kicking, and getting up.

Balanced Walking with Capture Steps

Bipedal walking is one of the most essential skills required to play soccer with humanoid robots. Superior walking speed and stability often gives teams the winning edge when their robots are the first at the ball, maintain ball control, and drive the ball towards the opponent goal with sure feet. In this contribution, we present an implementation of our Capture Step Framework on a real soccer robot, and show robust omnidirectional walking. The robot not only manages to locomote on an even surface, but can also cope with various disturbances, such as pushes, collisions, and stepping on the feet of an opponent. The actuation is compliant and the robot walks with stretched knees.

Online learning of foot placement for balanced bipedal walking

Due to the high complexity of the humanoid body, and its inherently unstable inverted pendulum-like dynamics, the development of a robust and versatile walking controller proves to be a difficult task. Using machine learning algorithms with hardware in the loop is a promising way of achieving balanced and dynamic gaits. In this work, we propose an online learning technique that learns how to step onto a reference footstep location while maintaining the balance of a bipedal walker in the presence of disturbances. The ability to step with the help of a parametrized motion generator simplifies the learning problem to the low-dimensional space of footstep coordinates. To quickly adapt the produced step sizes from learned experience, we update an online-capable function approximator with a pendulum-cart motivated gradient function that incorporates the trade-off between maintaining balance and stepping onto a desired location. While our method is able to robustly learn suitable footstep locations without prior knowledge, we gain advantage from initializing the learning with an analytic controller and show experimentally that the learning process can further improve the capabilities of the robot.

Learning footstep prediction from motion capture

Central pattern generated walking for bipedal robots has proven to be a versatile and easily implementable solution that is used by several robot soccer teams in the RoboCup Humanoid Soccer League with great success. However, the forward character of generating motor commands from an abstract, rhythmical pattern does not inherently provide the means for controlling the precise location of footsteps. For implementing a footstep planning gait control, we developed a step prediction model that estimates the location of the next footstep in Cartesian coordinates given the same inputs that control the central pattern generator. We used motion capture data recorded from walking robots to estimate the parameters of the prediction model and to verify the accuracy of the predicted footstep locations. We achieved a precision with a mean error of 1.3 cm.

Lateral Disturbance Rejection for the Nao Robot

Maintaining balance in the presence of disturbances is crucial for bipedal robots. In this paper, we focus on the lateral motion component. In order to attain disturbance rejection and to quickly recover balance, we combine three different control approaches. As a principal building block, we generate center of mass trajectories with a linear model predictive controller that takes scheduled footsteps into account. Strong disturbances generate unexpected angular momenta that can compromise stability. A second control layer extends the underlying preview controller with two recovery strategies that modify the planned CoM trajectories to dampen the rotational velocity of the robot and adapt the timing of the steps according to the expected orbital energy of CoM trajectories at support exchange. Experiments with a real Nao robot show that the system is able to recover from lateral disturbances as long as the robot does not tip over the current support leg.

RoboCup 2012 Best Humanoid Award Winner NimbRo TeenSize

Over the past few years, soccer-playing humanoid robots advanced significantly. Elementary skills, such as bipedal walking, visual perception, and collision avoidance have matured enough to allow for dynamic and exciting games. In this paper, team NimbRo TeenSize, the winner of the RoboCup 2012 Best Humanoid Award, presents its robotic platform and its approaches to perception and behavior control.

Learning to Improve Capture Steps for Disturbance Rejection in Humanoid Soccer

Over the past few years, soccer-playing humanoid robots have advanced significantly. Elementary skills, such as bipedal walking, visual perception, and collision avoidance have matured enough to allow for dynamic and exciting games. When two robots are fighting for the ball, they frequently push each other and balance recovery becomes crucial. In this paper, we report on insights we gained from systematic push experiments performed on a bipedal model and outline an online learning method we used to improve its push-recovery capabilities. In addition, we describe how the localization ambiguity introduced by the uniform goal color was resolved and report on the results of the RoboCup 2013 competition.

Designing effective humanoid soccer goalies

Most of the research related to the topic of falling strategies considers falling to be an unavoidable part of bipedal walking and is focused on developing strategies to avoid falls and to minimize mechanical damage. We take an alternative point of view and regard falling as a means to an end. We present our falling strategy for the specific case of a robot soccer goalie that deliberately jumps in front of a moving ball to prevent it from rolling into the goal. The jump decision is based on observed ball position, speed and direction of movement. We show how we implement a targeted falling into the appropriate direction, minimize the time from the jump decision to ground impact, and what solutions we developed to prevent mechanical damage. The presented falling technique was used in RoboCup Humanoid KidSize and TeenSize competitions and proved to be essential for winning.