The examples here demonstrate how to use constrained manipulation through the API. This feature is used to manipulate objects that are constrained by the environment, and move in a low-dimensional space. Examples include ball valves, switches, cabinets and drawers.
These examples need to be run with python3, and have the Spot SDK installed.
Constrained Manipulation Example
Before running this example, we need to set up the grasp of the object. Once the object is successfully grasped, we can run the constrained manipulation example.
Setup Robot and grasp and object
To properly setup for this example:
Use an external E-Stop endpoint from an api client or tablet
Power the robot on
Use the API or the tablet to drive the robot close to the object of interest, for e.g. a ball valve, cabinet, etc.
Grasp the object using the API scripts or the tablet
Run the run_constrained_manipulation.py script to manipulate constrained object.
Running the Example
When run, this script will take over the lease and perform manipulation for the task specified in the script for a fixed duration of time.
You can construct your task type of interest by calling one of the functions in constrained_manipulation_helper.py for e.g. construct_crank_task(velocity_normalized) in the run_constrained_manipulation.py script.
You can run constrained manipulation in either velocity or position mode.
In velocity mode, you specify the task type, the velocity along the task and the force or torque limits as arguments. Note that for all tasks, the velocity is a normalized velocity in the range [-1, 1]. Please look in the file constrained_manipulation_helper.py for more details on how the velocity is scaled with the force limit.
python3 run_constrained_manipulation.py ROBOT_IP --task-type crank --task-velocity 0.5 --force-limit 40
In position mode, you specify the task type, task velocity, target-angle or target-linear-position and the force or torque limits as arguments. For position mode, the task velocity is used as a velocity limit, i.e. the max velocity that the planned trajectory can reach. Note that this velocity is normalized according to the force/torque limit. Please look in the file constrained_manipulation_helper.py for more details on how the velocity is scaled with the force or torque limit. Note that some steady state error will exist in position control, so we recommend extensively testing your specific task and choosing the target displacement based on the performance.
python3 run_constrained_manipulation.py ROBOT_IP --task-type crank --force-limit 40 --task-velocity 0.5 --target-angle 3.14