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  • 70m0Hdt5y$z7R0nPi$nppJ2OtK`(82FSA-rguulAmQEZ!TG zrWQ>HuuXNVN&{aQpXIgMY2*9mj_U=xJB!y4>mXGyT6j5$b#VEscV*Mt-R5K>_ZsDv zkjo336+fF@n542bG-by|ir4O%B7?S*`ziZOc>j7}3-=l2{7-sL$YMqipsfV9F?Q5X zGy%aO4D|hrTJDbDYZI1ROf~8_&hWEo%2t3tfO4|#ttuHn+>)1URHqpq98#J76C1~R zH_D`$s_dKdaI}*j8zbX^yt2;DC^H1`)v$CYV_>r@3>$MCnWcxJ5=wEucRpq|47;|F z$;9eS2l7s4sW6`1(Io0xZY!&f#06ug`ONcF+n>WISI>4O&gm_6;0O#1v$QRDc--p<9PLE(M7>{(1E%keoCl4R*_o3Px1fHF@sC$oCZzJrvf; z1=e`^^U(7Q_x2-#_w$Pi#-!7)Iu42FPwengGRbsJ^uBC|+UY6fbLUd)Z0*~o`=mxH z_%5ck+dO+{TNI!2DCNjlA&1tq6D~i zg-P^cPxgN*c4eWM&7VflT&U+-@QyHsPqIZx^^k}uxZUZ-w>MHQAyZJ49#-5pGweS_ zVv4ya!NcU*2_}33%V?vexD0e@(bxruQO4pWbnelm_)m{^v|Whlf=suwi2oE#1fR=R z3{BK7CO$#$zRz-avH|BC083}{hCPccB@hp%;80-pPSwr-r!rv3#kt28z~pFR9(gcj zqvEHFV3xgmYTd(h&)A2;z8g~Z&-wY$C!qxnZ&Ofw*}3d^Jck05y2rQ!Si5l_ah~Bh zDc>c@YlUGT(M_;^k^fwu6w2TX>kC>B+A=2{)H?%Tp`> zy)tA0dznW@&goF$0<>UhEcE|fj>g4a=C#*(WsJS`_ +and by extension `dm_control `_ repositories. + +.. note:: + This section is implemented in ``examples/minimal_working_example.py``. + Run this file or any of the examples with the ``--gui`` option or ``-g`` + to run the simulation in the visualization app. + +A minimum working example consists of a robot configuration, an environment, +an agent that controls the robot, and a method to run the environment. +:py:class:`dm_robotics.panda.environment.PandaEnvironment` is the main point of interaction. +This class populates the environment with Panda robots configured using +:py:class:`dm_robotics.panda.parameters.RobotParams`. + +.. code:: python + + robot_params = params.RobotParams() + panda_env = environment.PandaEnvironment(robot_params) + + with panda_env.build_task_environment() as env: + ... + +The MoMa task environment returned by ``build_task_environment()`` can then be executed +by :py:func:`dm_robotics.panda.run_loop.run` or visualized with +:py:class:`dm_robotics.panda.utils.ApplicationWithPlot`. The visualization app +is a convenient tool to debug environments or evaluate agents and +includes live plots of observations, actions, and rewards. + +.. image:: img/gui.png + :alt: Visualization App + +Finally, we require an agent that provides actions in the form of NumPy arrays +according to the environment's specification. A minimal agent provides a ``step`` +function that receives a timestep and returns an action. + +.. code:: python + + class Agent: + + def __init__(self, spec: specs.BoundedArray) -> None: + self._spec = spec + + def step(self, timestep: dm_env.TimeStep) -> np.ndarray: + action = np.zeros(shape=self._spec.shape, dtype=self._spec.dtype) + return action + +The shape of the action will depend on the number of configured robots and chosen actuation mode. + + Motion Control --------------