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These two demos show an example we did in a case study aiming at verifying Ke Jia's
ability of planning for complex tasks. The complex tasks we chose are service
queries of following form: "move a from position B to C and move b from D to
E", given the initial state is shown in Fig. 1, where the robot is at location
A and portable objects a and b are in position B and D, respectively. For a
service robot that employs commands recognition technique, these two commands
will be performed successively and separately, as shown in Figure 2. Generally
this is not an optimal solution to the panning problem.
Ke Jia takes the user request as a single complex task and makes a plan
interleaving the execution of the two component tasks this way: goto(B),
pickup(a), goto(D), pickup(b), goto(C), putdown(a), goto(E), putdown(b), as
shown in Figure 3. This plan is optimal with respect to the cost of Ke Jia's
atomic actions, which comprises the distances between the locations/positions.
More importantly, this plan was made autonomously and completely with
general-purpose mechanisms. No matter whatever the locations/positions are
chosen in the environment, Ke Jia is always able to generate an optimal plan for
this complex task. This also means that Ke Jia can understand the
interconnection among the atomic tasks contained in the complex task through her
NLP mechanism.
Figure 1. The initial and the goal state of the complex task ![]() Figure 2. Perform two component tasks separately ![]() Figure 3. Perform the complex task optimally In the experiments, Ke Jia was given the task in more natural forms, such as an English sentence like "give me the green bottle and put the red bottle on the table." The real-time performance is also acceptable. Generally, it took about 0.8 second for Ke Jia to accomplish the task panning. The computation of other modules is less time-consuming. |
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