**3.2.1 Whole-body motion**

Humans have a large repertoire of motor actions to move the whole body from one point to another. Walking could be considered the most representative behavior in this repertoire but we learn to adapt and use our bodies depending on the circumstances. Humans are also able to run, crawl, jump, climb or descend stairs, and if using the arms, then we can squeeze our bodies between narrow spaces or if in water we can learn to swim.

Rob realizes that researchers in whole body motion for humanoid robots have focused most, if not all their attention in walking only. The most common approach to control the walking behavior and balance of a humanoid robot is called Zero Moment Point (ZMP) and it has been used since the beginning of humanoid robotics research (Vukobratovic & Borovac, 2004). This approach computes the point where the whole foot needs to be placed in order to have no moment in the horizontal direction. In other words, ZMP is the point where the vertical inertia and gravity force add to zero. Most state of the art humanoid platforms like ASIMO, HRP-4, and HUBO2 make use of this approach. The main drawbacks of ZMP arise from the need to have the whole foot in contact with a flat surface, and it assumes that this surface has enough friction to neglect horizontal forces.

Passive-dynamic walkers are an alternate approach to humanoid locomotion (Collins et al., 2005). These platforms try to exploit not only the non-linear properties of passive spring-damper components but the interaction of the whole body with the environment. The result is, in most cases, a more human-like gait with a heel-toe step in contrast to the flat steps seen in ZMP-based platforms. State of the art passive-dynamic walkers use dynamic balance control (Collette et al., 2007) which provides them with robustness to external disturbances and more human-like whole-body motor reactions. Examples of this approach can be found in platforms such as Dexter (Anybots, 2008), PetMan (Petman, 2011), and Flame (Hobbelen et al., 2008).

Rob also found out that running and jumping have already been implemented in a few of the current humanoid platforms (Anybots, 2008; Niiyama & Kuniyoshi, 2010), although there is still much work to do to reach human-like levels. Once humanoid robotics started to become the meeting point of different scientific groups such as developmental psychologists, neuroscientists and engineers, interesting topics emerged. One of them was the study of infant crawling, its implementation in a humanoid platform was done using the iCub robot (Righetti & Ijspeert, 2006), Fig. 1(d).

For our roboticist Rob, the challenge of making humanoid robots replicate the different types of motor behaviors found in humans is just one part of a larger challenge. An equally

for Humanoid Robots 9

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(a) Shadow Hand (b) TwendyOne's hand

(c) ELU2's hand (d) Ishikawa-Oku's Lab manipulator

time, the exponential growth of computational power gives enough freedom to integrate all

In terms of actuators, Rob is impressed by the results from the use of spring-damper components. Previous attempts to control this kind of materials gave many researchers (especially to those working with traditional control theory) several headaches. However current implementations for whole-body motion and manipulation have shown the feasibility of this approach, thanks most likely to the use of alternative methodologies, e.g. nonlinear

Having considered his mechanical requirements, Rob now turns to computational requirements for his robot. In his mind, this means anything that could be needed to make the hardware perform interesting behaviors. This includes, but is not limited to, learning how to solve (not necessarily pre-defined) tasks in uncertain environments under changing or

Within robotics, Rob has distinguished two main approaches of interest to him: one which we shall call the traditional symbolic approach (also known as *Cognitivism*) and one that we will name embodied approach (also known as *Emergent Systems*). Although both approaches can sometimes use the same technologies (neural networks are a prime example of that), they differ philosophically in the sense that symbolic is first and foremost an AI approach

undefined conditions and interacting with humans in a non-trivial manner.

Fig. 3. Samples of different state of the art manipulators.

this information.

dynamical systems.

**4. Computational requirements**

interesting project will be the design of a decision making control that allows the agent to switch between the different motor behaviors. Switching between walking to crawling and vice versa, from walking to trotting or running and back. Those changes will need to be generated autonomously and dynamically as a response to the needs of the environment. The traditional way of programming a robot by following a set of rules in reaction to external stimuli does not work and will not work in dynamic, unconstrained environments. Rob is thinking about Asimo tripping over a step and hitting the floor with his face first. Rob knows that a more dynamic, autonomous and adaptive approach is needed for the future of his humanoid robot.
