What techniques exist for the software-driven locomotion of a bipedal robot?

Question

I'm programming a software agent to control a robot player in a simulated game of soccer. Ultimately I hope to enter it in the RoboCup competition.

Amongst the various challenges involved in creating such an agent, the motion of it's body is one of the first I'm facing. The simulation I'm targeting uses a Nao robot body with 22 hinge to control. Six in each leg, four in each arm and two in the neck:

_{(source: sourceforge.net)}

I have an interest in machine learning and believe there must be some techniques available to control this guy.

At any point in time, it is known:

• The angle of all 22 hinges
• The X,Y,Z output of an accelerometer located in the robot's chest
• The X,Y,Z output of a gyroscope located in the robot's chest
• The location of certain landmarks (corners, goals) via a camera in the robot's head
• A vector for the force applied to the bottom of each foot, along with a vector giving the position of the force on the foot's sole

The types of tasks I'd like to achieve are:

• Running in a straight line as fast as possible
• Moving at a defined speed (that is, one function that handles fast and slow walking depending upon an additional input)
• Walking backwards
• Turning on the spot
• Running along a simple curve
• Stepping sideways
• Jumping as high as possible and landing without falling over
• Kicking a ball that's in front of your feet
• Making 'subconscious' stabilising movements when subjected to unexpected forces (hit by ball or another player), ideally in tandem with one of the above

For each of these tasks I believe I could come up with a suitable fitness function, but not a set of training inputs with expected outputs. That is, any machine learning approach would need to offer unsupervised learning.

I've seen some examples in open-source projects of circular functions (sine waves) wired into each hinge's angle with differing amplitudes and phases. These seem to walk in straight lines ok, but they all look a bit clunky. It's not an approach that would work for all of the tasks I mention above though.

Some teams apparently use inverse kinematics, though I don't know much about that.

So, what approaches are there for robot biped locomotion/ambulation?

---

As an aside, I wrote and published a .NET library called TinMan that provides basic interaction with the soccer simulation server. It has a simple programming model for the sensors and actuators of the robot's 22 hinges.

You can read more about RoboCup's 3D Simulated Soccer League:

• http://en.wikipedia.org/wiki/RoboCup_3D_Soccer_Simulation_League
• http://simspark.sourceforge.net/wiki/index.php/Main_Page
• http://code.google.com/p/tin-man/

Answer 1

There is a significant body of research literature on robot motion planning and robot locomotion.

General Robot Locomotion Control

For bipedal robots, there are at least two major approaches to robot design and control (whether the robot is simulated or physically real):

• Zero Moment Point - a dynamics-based approach to locomotion stability and control.
• Biologically-inspired locomotion - a control approach modeled after biological neural networks in mammals, insects, etc., that focuses on use of central pattern generators modified by other motor control programs/loops to control overall walking and maintain stability.

Motion Control for Bipedal Soccer Robot

There are really two aspects to handling the control issues for your simulated biped robot:

1. Basic walking and locomotion control
2. Task-oriented motion planning

The first part is just about handling the basic control issues for maintaining robot stability (assuming you are using some physics-based model with gravity), walking in a straight-line, turning, etc. The second part is focused on getting your robot to accomplish specific tasks as a soccer player, e.g., run toward the ball, kick the ball, block an opposing player, etc. It is probably easiest to solve these separately and link the second part as a higher-level controller that sends trajectory and goal directives to the first part.

There are a lot of relevant papers and books which could be suggested, but I've listed some potentially useful ones below that you may wish to include in whatever research you have already done.

Reading Suggestions

LaValle, Steven Michael (2006). Planning Algorithms, Cambridge University Press.

Raibert, Marc (1986). Legged Robots that Balance. MIT Press.

Vukobratovic, Miomir and Borovac, Branislav (2004). "Zero-Moment Point - Thirty Five Years of its Life", International Journal of Humanoid Robotics, Vol. 1, No. 1, pp 157–173.

Hirose, Masato and Takenaka, T (2001). "Development of the humanoid robot ASIMO", Honda R&D Technical Review, vol 13, no. 1.

Wu, QiDi and Liu, ChengJu and Zhang, JiaQi and Chen, QiJun (2009). "Survey of locomotion control of legged robots inspired by biological concept ", Science in China Series F: Information Sciences, vol 52, no. 10, pp 1715--1729, Springer.

Wahde, Mattias and Pettersson, Jimmy (2002) "A brief review of bipedal robotics research", Proceedings of the 8th Mechatronics Forum International Conference, pp 480-488.

Shan, J., Junshi, C. and Jiapin, C. (2000). "Design of central pattern generator for humanoid robot walking based on multi-objective GA", In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1930–1935.

Chestnutt, J., Lau, M., Cheung, G., Kuffner, J., Hodgins, J., and Kanade, T. (2005). "Footstep planning for the Honda ASIMO humanoid", Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), pp 629-634.

Answer 2

I was working on a project not that dissimilar from this (making a robotic tuna) and one of the methods we were exploring was using a genetic algorithm to tune the performance of an artificial central pattern generator (in our case the pattern was a number of sine waves operating on each joint of the tail). It might be worth giving a shot, Genetic Algorithms are another one of those tools that can be incredibly powerful, if you are careful about selecting a fitness function.

Answer 3

Here's a great paper from 1999 by Peter Nordin and Mats G. Nordahl that outlines an evolutionary approach to controlling a humanoid robot, based on their experience building the ELVIS robot:

• An Evolutionary Architecture for a Humanoid Robot

Answer 4

I've been thinking about this for quite some time now and I realized that you need at least two intelligent "agents" to make this work properly. The basic idea is that you have two types intelligent activity here:

1. Subconscious Motor Control (SMC).
2. Conscious Decision Making (CDM).

Training for the SMC could be done on-line... if you really think about it: defining success within motor control is basically done when you provide a signal to your robot, it evaluates that signal and either accepts it or rejects it. If your robot accepts a signal and it results in a "failure", then your robot goes "offline" and it can't accept any more signals. Defining "failure" and "offline" could be tricky, but I was thinking that it would be a failure if, for example, a sensor on the robot indicates that the robot is immobile (laying on the ground).

So your fitness function for the SMC might be something of the sort: numAcceptedSignals/numGivenSignals + numFailure

The CDM is another AI agent that generates signals and the fitness function for it could be: (numSignalsAccepted/numSignalsGenerated)/(numWinGoals/numLossGoals)

So what you do is you run the CDM and all the output that comes out of it goes to the SMC... at the end of a game you run your fitness functions. Alternately you can combine the SMC and the CDM into a single agent and you can make a composite fitness function based on the other two fitness functions. I don't know how else you could do it...

Finally, you have to determine what constitutes a learning session: is it half a game, full game, just a few moves, etc. If a game lasts 1 minute and you have a total of 8 players on the field, then the process of training could be VERY slow!

Update

Here is a quick reference to a paper that used genetic programming to create "softbots" that play soccer: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.136&rep=rep1&type=pdf

With regards to your comments: I was thinking that for the subconscious motor control (SMC), the signals would come from the conscious decision maker (CDM). This way you're evolving your SMC agent to properly handle the CDM agent's commands (signals). You want to maximize the up-time of the SMC agent regardless of what the CDM agent says.

The SMC agent receives an input, for example a vector force on a joint, and it then runs it through its processing unit to determine if it should execute that input or if it should reject it. The SMC should only execute inputs that it doesn't "think" it will recover from and it should reject inputs that it "thinks" would lead to a "catastrophic failure".

Now the SMC agent has an output: accept or reject a signal (1 or 0). The CDM can use that signal for its own training... the CDM wants to maximize the number of signals that the SMC accepts and it also wants to satisfy a goal: a high score for its own team and a low score for the opposing team. So the CDM has its own processing unit that is being evolved to satisfy both of those needs. Your reference provided a 3-layer design, while mine is only a 2-layer... I think mine was a right step in towards the 3-layer design.

One more thing to note here: is falling really a "catastrophic failure"? What if your robot falls, but the CDM makes it stand up again? I think that would be a valid behavior, so you shouldn't penalize the robot for falling... perhaps a better thing to do is penalize it for the amount of time it takes in order to perform a goal (not necessarily a soccer goal).