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Running Bioloid Demos †

These are demonstrations with a real robot.

Overview †

The robot employed here is Bioloid, made by ROBOTIS. To execute the following demos, at least, Bioloid Comprehensive Kit is needed. However, you can use the other type of Bioloid Kit by changing the agent scripts of these demos.

Note: we do not guarantee that the demos work with a real robot (but can advise a way to improve :-).

We use the King Spider model of Bioloid.

A crawling task is performed with this robot.

The objective of the crawling task is to move forward as far as possible. So, the reward is designed so that it is proportional to the velocity of the robot. Bioloid has an infrared ray (IR) sensor to observe the distance from the robot to an obstacle. We use the IR sensor and calculate the velocity for the reward.

The following figure shows a setup of the environment.

Each episode begins with an initial pose, and ends if t>50[s], the robot touches the wall (determined by the operator), or some problems arise.

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Build the Demo Program †

Execute:

 $ cd benchmarks/bioloid
 $ make

See Documentation/Installation Guide for the detail.

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Running Command †

Please read Common Usage in advance. The running command is:

 $ ./DEMO_PRG -path PATH_LIST -agent AGENT_FILE -outdir OUT_DIR

The demo-specific elements are:

DEMO_DIR: bioloid (benchmarks/bioloid).
DEMO_PRG: bioloid.out
PATH_LIST: ../cmn,m,m/cr
AGENT_FILE: Available agent scripts are listed below.
OUT_DIR: Any directory is possible.

For example, execute the following:

 $ mkdir -p result/rl1
 $ ./bioloid.out -path ../cmn,m,m/cr -agent ql_dcob1 -outdir result/rl1

To exit the program, press Ctrl+c on the terminal, then press 'Q' (shift+q).

In OUT_DIR (result/rl1), the result files are stored. For example, use gnuplot to plot the learning curve as:

 $ gnuplot
 gnuplot> plot 'result/rl1/log-eps-ret.dat' w l

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Agent Script †

The following files can be specified as AGENT_FILE.

ql_dcob1: 5-DoF, Q(lambda)-learning, DCOB (action space), linear action value function (NGnet).
ql_dcob2: 2-DoF, Q(lambda)-learning, DCOB (action space), linear action value function (NGnet).
ql_dcob3: 3-DoF, Q(lambda)-learning, DCOB (action space), linear action value function (NGnet).
ql_grid1: 5-DoF, Q(lambda)-learning, Grid action space, linear action value function (NGnet).
z1: For the Dinosaur model of Bioloid (5-DoF).