3D-Printed Biologically-Inspired Robotics Using Arduino

3D Printed Biologically Inspired Robotics circuit
I have been spending the last few months doing research into biologically inspired robotic structures. While my approach may seem formalistic in nature, these devices were simply a means for ultimately conducting social research. This desire stems from my experience building Simple Bots, and their subsequent display at multiple Maker Faires. 

While displaying these robots, I observed that the thousands of people who interacted with them, projected their own social realities upon these devices which were little more than motors zip tied to plastic household utensils. The obvious shortcoming of the Simple Bots approach was that no matter what personalities people projected upon these creations, they ultimately implicitly understood that these creatures were robotic.

This led me to wonder what would happen if I built robots that were more intentionally organic-like and fluid in motion. Would people perceive them as being even more alive? Was there a threshold where people would stop perceiving them as robots and start perceiving them as living organisms? However, before I could answer these questions, I needed to figure out the mechanics that would allow these motions.

While I could have explored a number of different fabrication processes, I recently found myself with unlimited access to eight Objet Connex 500 3D printers. Aside from having an incredibly high print resolution, what makes these printers unique is their ability to print digital materials with a wide range of hardnesses and colors. These printers essentially allow the different materials to be mixed together to create a Pantone-like scale for material hardness. This was particularly compelling for this type of robotics because it would provide the ability to print highly accurate assemblies that simultaneously contained rigid and flexible materials. By printing materials with different hardness, flex, stretch, and torsion properties, I would be able to print life-like joints and musculature. With this in mind, I set out to make biologically inspired designs using 3D printing technologies.

All digital models were created using 123D Design on account of its ease of use, and ability to be downloaded and used for free. This is an intentional decision to make the project open, and modifiable. It is my hope that others will be able to download my files, iterate upon my solutions, and ultimately expand my research. All content contained herein is licensed with a Creative Commons 2.5 Share-Alike Non-Commercial Attribution license.

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3D Printed Biologically Inspired Robotics circuit

Step 1: A Note on Evolution

When I started to write this guide, my initial intent was to call it “How Not to Build a 3D Printed Robot.” When explaining this desire to a colleague, I arrived at a new potential title which was “Failures of Robotic Evolution.” However, comparing robotic design to evolution is problematic in a number of regards, and opens up a large can of worms. As Steven Vogul arguest in “Cats’ Paws and Catapults,” the design and evolutionary processes should not be conflated. 

Most notably, Vogul points out:

  • “nature is not only glacial in speed, but lacking in versatility”
  • “most variations [mutations] are either neutral or detrimental”
  • “innovation comes hard, and once achieve it disseminates entirely within a lineage”
  • “diversity in nature represents superficial features of an exceedingly conservative and stereotyped character”

He then goes on to explain how for human innovation, design is a much better model because fundamental design change faces no evolutionary barriers. For instance, a design can be fundamentally changed without taking into consideration the need for each subsequent iteration to allow perfect functionality to perpetuate the continuity of its lineage. Nor do we have to account for growth or scaling of the object over its lifespan. Humans are only limited by the materials at hand, the existing knowledge base, and ingenuity. Humans are also free to borrow, adapt, and remix designs at will. Most challenges for human design innovation tend to be social and/or legal, not formalistic.

While it may be tempting to describe robotics in terms of evolution, I think it is better to view it as an iterative process. This process, while very similar to evolution, is noticeably different. As evolution repeatedly demonstrates, most change is detrimental, blind and slow. Iteration, on the other hand, is (ideally) beneficial, intentional, and fast. One rarely iterates if they don’t think the subsequent version is going to be an improvement or will illuminate something that will help to move other iterations forward. Just as organic-like machines are similar to life, but not replicating it; so is the case that the process for making these machines should be evolution-like, but not a perfect repetition.


Step 2: Research Methodology

One last thing I must make note of before I proceed is that the research contained herein is “artistic research.” I make this distinction to highlight that my goals and methodologies as an artist are not that of the engineer or scientist. I often like to point out to people that as an artist I am free to explore the most interesting solution, not necessarily the best solution. In fact, very rarely is it the most interesting solution the best solution. I also have the freedom to explore many pathways, and make what scientists would consider leaps of faith. So, as you read this, please keep in mind that I am aware that my approach is not truly scientific, many of these technical issues are solved problems, and much more efficient mechanical solutions may exist. The most efficient mechanical solution is not what I am interested in exploring, and it is not my goal to repeat this prior work. 

In doing this, it is my goal to create robotic agents that can help us better understand the relationships between ourselves and our technology. While it may seem like I got lost in a web of formalistic considerations, please bear in mind that my ultimate goal is to make abstracted biologically-inspired robots intended to capitalize on our existing mental associations, and social relationships. I am not trying to explicitly further the field of engineering, nor am I trying to exclusively understand abstract social constructs. My goal is to find a holistic approach that acknowledges both, but does not favor either.

As Simon Penny points out in Bridging Two Cultures (2005):

“It [machine artwork] is interdisciplinary because it pursues technical research which exceeds the constraints of the objectivist-positivist pursuit of knowledge per se and likewise exceeds the base constraints of production of technological commodities for market, because it is motivated by and integrated into larger socio-cultural flows.”

Hopefully some of what I share here will help contribute to and push forward ‘more serious’ objectivist-positivist scientific inquiry. More importantly, I hope to provide people with tools and techniques which can be used a springboard for better understanding our relationships with technological organisms. This will ideally help us to have richer social experiences with technology, and live happier and fuller lives.


Step 3: The Uncanny Valley and Social Interaction

When engaging with robots, we always bring to the table a wealth of social knowledge and history with us. This makes any interaction we have with robots tainted not only by our own preconceptions of robotics, but also by our experience of the natural world. The closer a robot mimics something else, the more associations we are able to draw from and place upon it. Since a robot is basically a mirror of our preconceptions, in some regard, any manner in which a robot behaves is correct. It should not matter whether it is flailing helplessly on the floor, or falling apart. After all, both the concept of “flailing helplessly” and “falling apart” are social constructs arrived at from our understanding of and interaction with other organisms and machinery. Regardless of what the robot does, or its own level of cognizance, we will project our social reality upon it. 

Additionally, beyond our own social constructs, we bring with us a nagging meta-cognition that this being is not alive in the same sense that we are. This understanding can both enable us to accept it more readily as an autonomous creature in its own right, but also lead us to reject it as a cheap simulation. As roboticist Masahiro Mori first claimed in 1970, an “uncanny valley” in human empathy levels exists at threshold in which a robot becomes very human-like, and then disappears entirely when perfect replication is achieved. In other words, we have empathy for robots until the point at which it is very close to being human, at which point we have a strong revulsion. Should the robot continue becoming more human-like (to the point of being indistinguishable from human), our empathy levels spike up to human-to-human empathy levels. This sudden and drastic dip where our empathy levels drop dramatically before spiking up again, is the “uncanny valley”

For example, this unsettling humanoid robot falls deep within the “uncanny valley:”

While this principle is largely applied to humanoid robots, I would venture to guess the same principle can be applied – to a large extent – to zoomorphic robots. After all, regardless of whether animals have rich cognitive and emotional lives (which I personally believe some do), we project ours upon them, and in some capacity humanize them. I would venture to guess a nearly perfect robotic dog, would be just as unsettling as a nearly perfect robotic human.

Understanding that I will never achieve entirely perfect zoomorphic replicas, I have decided not to set my sights on it. It is my worry that “almost perfect” execution might actually be worse than an abstracted mimesis. Instead of direct replication, I have set out to create what Simon Penny termed a “Social Agent.” These agents exist within a social environment, as a sort of reflection of the viewer. People observe them and then project their own cultural metaphors and associations upon them, creating cultural meaning where none really exists. For this reason, a toothbrush head with a battery and vibrating motor attached could be perceived to be alive – even playful – although it has no real intelligence of what to speak. Capitalizing on people’s need to understand and bring meaning to the world, these devices exploit people’s prior experiences with other living creatures to offer meaning.

In this circumstance, the robotic agent actually benefits from behaving like other organisms, but not actually trying to be any of them. For starters, it can capitalize on people’s existing cultural association, and secondly, it does not risk falling into the “uncanny valley.” Therefore, it was not my goal to recreate existing forms. There was no point. The biological organisms that I am studying already exist in a perfectly fine manner. Instead, it is my goal to capitalize on the existing social responses these forms trigger in humans. This could easily be done without perfect replication.

Rather than recreate a spider or squid, I have set out to create a new “species” which could be perceived as spider-like or squid-like. Ultimately, I am building robotic creatures which could be perceived as autonomous – perhaps even intelligent – sentient organisms, but have little to no intelligence to speak of. In other terms, I strive for the robots to be perceived as living in a social context, rather than to exist as self-aware living organisms that try to be alive. Ultimately, it is not my goal to get behavioral responses out of robots, but behavioral responses out of humans.


Step 4: Predictable Unpredictability

As I wrote previously in my Simple Bots book, I like to consider the most important property that a robot needs to posses to be “predictable unpredictability.” What I mean by this is that most living creatures behave in a few fundamental ways most of the time, but have little deviations or “ticks” which deviate from this behavior. These deviations and ticks are predictable in that if the organism did not behave in this way, we would perceive them as entirely predictable and mechanical. This leads us to conclude that an organism is predictable, except when it is not. We cannot predict when it is not, but we can easily predict that it is going to have this unpredictability. Therefor, living organisms have an unpredictability which is predictable. 

As we relate to most other animals that behave in this manner, so can we also relate to robots. If motorized jumbles of zip tied plastic can convince people that they are displaying life-like behaviors, then this would lead me to believe that by making them even more life-like in form, I can convince people that these organisms are even more alive. Perhaps these creatures – which exist nowhere in nature – can even inspire people to take the leap of faith required to perceive them as entirely living.

Ultimately, I am building machines to mimic attributes of living organisms in order to act upon humans associations and force emotional reactions. On some level, this is an absurd interaction in which intelligent organisms empathize with a machine that has no cognition whatsoever.


Step 5: Initial Biomechanical Models

As my point of departure, I centered my initial research around the forms of spiders, human arms, and cephalopod tentacles. I selected these three due to my initial hypothesis that they existed along a scale of fluidity; whereas the spider with its exoskeleton is the most rigid in its biomechanics, the cephalopod with no tangible skeleton of which to speak is the most fluid, and the human arm with its internal skeleton is somewhere in the middle. Additionally, all three mechanisms simply fascinated me, and I wanted to to understand how they functioned. Having this understanding would help me to decide how best to construct my social robots.

Step 6: The Arm

Whereas the part of the arm that is probably the most studied and replicated in robotics is the human hand and wrist, the part that has most fascinated me is the shoulder. This fascination stems from having torn apart the inner structure of my right shoulder and needing to have it surgically reconstructed and held together by about a dozen bolts. My injury was called “360 Degree Instability,” and what is telling about the name of this injury is that the shoulder has a very large range of motion. In fact, it is undoubtedly the human joint capable of the widest range of motions. 

There are 24 muscle groups that contribute to the movement and stability of the shoulder. Of these, seventeen are in some way attached to the shoulder blade, which is a free floating bone that both counterbalances and provides a suport for the arm’s rotation in the ball socket. There is only one traditional bone joint connecting the shoulder to the rest of the body (from the collarbone to the breastbone). Otherwise, the shoulder is largely held in place by tendons, ligaments, cartilage and muscle. Also worth noting, many of these muscles are long muscles that stretch down the length of the back and chest.

To translate these into robotic terms would have necessitated a larger support structure than the small modular joints I intended to build. Additionally, even if I were to simplify the shoulder musculature to only include the muscles required for forceful motion, I still would have needed a significant number of artificial muscles. It was my conclusion that as a point of departure towards a generalized human joint, the shoulder was less than ideal due to its uniqueness and complexity in structure.

Fortunately, not much further down the arm, one finds the elbow. The elbow is a textbook hinge joint. It also has a relatively simple pairing of muscles, which are responsible for expanding and contracting the joint along a 140 degree arc. The other notable characteristic of this hinge joint is that it not only mirrors a number of other human joints including the knee, and finger joints, but also mirrors hinge joints in a host of other species. These include, but are no means limited to lizards, dogs, horses, turtles, and elephants. In terms of a mechanism that would be key for generalized life-like locomotion, the hinge joint seemed like a good starting point.

The joint itself is relatively basic. It consists of two bones separated by cartilage and held together by four ligaments. Muscles then attach to the bones with tendons to create a third-class lever.  By contracting and releasing the muscles the forearm lifts and lowers. Beyond that, there is not too much more to understand in the way of basic functionality. It is a very simple biomechanical model, which makes it an ideal form to explore in terms of robotics.

In case you are unfamiliar with your own elbow, check this out:

Step 7: The Spider Leg

The next biomechanics model that I focused upon was the anatomy of spiders. I was surprised to discover that spiders were partially “hydraulic.” By regulating pressure within its body, the spider is able to create large amounts of torque relative to its body size. Interestingly, the reasons spiders curl up when the die is because of the loss of this pressurization. 

Most joints in the spider are controlled by traditional flexor and extensor muscles. However, two of the joints only have the presence of flexor muscles. By regulating the pressure in these joints and using muscles to pull against them, the spider is able to generate substantial force. These joints also have mechanical reversible properties, which means that the joints can not only stiffen to provide support, but can also aid in creating a frame for bend deformations in multiple directions. This use of muscle to create a structural frame is similar to the manner in which cephalopod tentacles operate.

Albeit my initial guesstimation was that a spider’s leg consisted of 5 segments, I was surprised to learn that it actually has 7 discrete segments. Additionally, each segment has a unique and considerable range of motion. This makes the spider leg much more robust than I initially anticipated.

In terms of the type of joints that comprise its leg – aside from the two partially hydraulic joints – the structure is not very remarkable. The spider’s joints consist entirely of hinge joints, like the one found in the human elbow. However, what sets them apart from human joints is the ability for a number of them to significantly bend along 2 axis.

From this basic research, it has become clear that in order to properly mimic a spider’s leg, the robot is going to need a heckuva lot of segments and actuators. Alternately, if the goal is not to perfectly mimic a spider, but to build a modular joint, then it will not look remarkably different from the human elbow joint. That said, I found the partially hydraulic joints very interesting for further research. Much of my early research revolved around the attempt to create hinge joints that included hydraulic elements.

Step 8: The Tentacle

3D Printed Biologically Inspired Robotics

The most fascinating biomechanic that I explored was the tentacle. A tentacle is a muscular hydrostatic support system. The tentacle structure consists entirely of dense three dimensional array of muscle. Typically this consist of two groupings; one of which is a bundle of long longitudinal muscles, and the other outer group is arranged in a slanted manner around the inner bundle. Volume within the tentacles is constant. Any decrease of muscle in any given direction must result in an increase in another. The muscles are arranged such that all three dimensions can be actively controlled.

The three main ways to describe tentacle movement include:

Reach – Increase in distance between proximal and distal portions of the tentacle.
Pull – Decrease in distance between proximal and distal portions of the tentacle.
Explore – No change in distance. Laterial movements with localized sharp bending and torsion

They are also capable of four basic deformations which can occur at any point at any time. These deformations are elongation, shortening, bending, and torsion (twisting). These deformations occur at “pseudo joints,” which are localized arm bends that serve as pivot points.

When underwater, Cephalopods are roughly neutrally buoyant, and are typically marginally denser than sea water. On account of their bouncy and range of motion, they are capable of a highly dynamic behavior, and are well adapted to this environment.

Here are some examples of octopi moving in water:

Since most of the research centered around these organisms focus on their movement in an aquatic environment, it only later dawned upon me that the dynamism of cephalopods was severely limited by gravity when the creature was removed from water. Albeit I could not find any research supporting this idea, I was able to find a number of videos showing how octopi move outside of water. My observations of these videos have led me to conclude that my initial hypothesis was a fairly accurate assumption.

Step 9: Robotic Precedents

Even though I tried to center my research exclusively around biomechanics and avoid looking at prior robots, I found looking at prior art pretty much unavoidable. All three biomechnical models have a rich history in robotics. Ultimately, I found it beneficial to examine prior work, and asses the pros and cons. Follows are some of the more interesting approaches I found.

Hydraulic Spider Prototype (2011)
Fraunhofer IPA

The robotic spider pictured above is built by Fraunhofer. Unlike other robotic spiders, this one mirrors actual spider biomechanics by using hydraulic joints. These joints are controlled by an on-board pump, valves, and control units. It is also of interest, because it using an SLS 3D printing process (similar in ways to the Objet printing process). The amount of control and force each leg is able to apply allows it to tread uneven ground with great agility. Unfortunately, there is limited documentation currently published, and no video demonstrating its abilities.

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