Attention

This challenge has ended!

This documentation is only for the Real Robot Challenge 2020 which has ended. Following challenges have their own documentation, see the challenge website for more information.

The SimFinger Class

The SimFinger class provides a simulation environment for the different finger robots. Note that for this challenge, only the finger type “trifingerpro” is used.

For the challenge, SimFinger should not be used directly but instead the wrapper class TriFingerPlatform should be used. Therefore the documentation here is limited to the relevant methods which are exposed through TriFingerPlatform.

API Documentation

class rrc_simulation.SimFinger(finger_type, time_step=0.004, enable_visualization=False)

A simulation environment for the single and the tri-finger robots. This environment is based on PyBullet, the official Python wrapper around the Bullet-C API.

Action(torque=None, position=None)

Fill in the fields of the action structure.

This is a factory go create an Action instance with proper default values, depending on the finger type.

Parameters

• torque (array) – Torques to apply to the joints. Defaults to zero.

• position (array) – Angular target positions for the joints. If set to NaN for a joint, no position control is run for this joint. Defaults to all NaN.

Returns

the action to be applied to the motors

Return type

Action

append_desired_action(action)

Pass an action on which safety checks will be performed and then the action will be applied to the motors.

Parameters

action (Action) – Joint positions or torques or both

Returns

The current time index t at which the action is applied.

Return type

int

get_current_timeindex()

Get the current time index.

get_timestamp_ms(t)

Get timestamp of time step ‘t’.

Parameters

t – Index of the time step. The only valid value is the index of the current step (return value of the last call of append_desired_action()).

Returns

Timestamp in milliseconds. The timestamp starts at zero when initializing and is increased with every simulation step according to the configured time step.

Raises

ValueError – If invalid time index t is passed.

get_desired_action(t)

Get the desired action of time step ‘t’.

Parameters

t – Index of the time step. The only valid value is the index of the current step (return value of the last call of append_desired_action()).

Returns

The desired action of time step t.

Raises

ValueError – If invalid time index t is passed.

get_applied_action(t)

Get the actually applied action of time step ‘t’.

The actually applied action can differ from the desired one, e.g. because the position controller affects the torque command or because too big torques are clamped to the limits.

Parameters

t – Index of the time step. The only valid value is the index of the current step (return value of the last call of append_desired_action()).

Returns

The applied action of time step t.

Raises

ValueError – If invalid time index t is passed.

get_observation(t)

Get the observation at the time of applying the action, so the observation actually corresponds to the state of the environment due to the application of the previous action.

This method steps the simulation!

Parameters

t – Index of the time step. The only valid value is the index of the current step (return value of the last call of append_desired_action()).

Returns

Observation of the robot state

Return type

Observation

Raises

ValueError – If invalid time index t is passed.

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class rrc_simulation.Action(torque, position, kp=None, kd=None)

Robot action.

The length of the attributes depends on the robot type. In the following n_joints is the number of joints and n_fingers the number of fingers (e.g. for the TriFinger robots n_fingers = 3, n_joints = 9).

torque

Torque commands for the joints.

Type

array, shape=(n_joints,)

position

Position commands for the joints. Set to NaN to disable position control for the corresponding joint.

Type

array, shape=(n_joints,)

kp

P-gain for position controller. Set to NaN to use default gain for the corresponding joint.

Type

array, shape=(n_joints,)

kd

D-gain for position controller. Set to NaN to use default gain for the corresponding joint.

Type

array, shape=(n_joints,)

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class rrc_simulation.Observation

Robot state observation.

The length of the attributes depends on the robot type. In the following n_joints is the number of joints and n_fingers the number of fingers (e.g. for the TriFinger robots n_fingers = 3, n_joints = 9).

position

Angular joint positions in radian.

Type

array, shape=(n_joints,)

velocity

Joint velocities in rad/s.

Type

array, shape=(n_joints,)

torque

Joint torques in Nm.

Type

array, shape=(n_joints,)

tip_force

Measurement of the push sensors on the finger tips.

Type

array, shape=(n_fingers,)