This directory contains the Python interfaces necessary to specify Task Space Regions (TSRs). For a detailed description of TSRs and their uses, please refer to the 2010 IJRR paper entitled "Task Space Regions: A Framework for Pose-Constrained Manipulation Planning" by Dmitry Berenson, Siddhartha Srinivasa, and James Kuffner. A copy of this publication can be downloaded here.
A TSR is typically used to defined a constraint on the pose of the end-effector of a manipulator. For example, consider a manipulator tasked with grabbing a glass. The end-effector (hand) must be near the glass, and oriented in a way that allows the fingers to grab around the glass when closed. This set of workspace constraints on valid poses of the end-effector can be expressed as a TSR.
A TSR is defined by three components:
T0_w- A transform from the world frame to the TSR frame wTw_e- A transform from the TSR frame w to the end-effectorBw- A 6x2 matrix of bounds on the coordinates of w
The first three rows of Bw bound the allowable translation along the x,y and z axes (in meters). The last three rows bound the allowable rotation about those axes in w frame. The rotation is expressed using the Roll-Pitch-Yaw (RPY) Euler angle convention and has units of radians.
Note that the end-effector frame is a robot-specific frame. In OpenRAVE, you can obtain the pose of the end-effector using GetEndEffectorTransform() on the manipulator. This is not equivilent to calling GetTransform() on the end-effector frame because these transformations differ by GetLocalToolTransform().
The following code snippet visualizes the end-effector frame of the robot's right arm:
ipython> import openravepy
ipython> h = openravepy.misc.DrawAxes(env, robot.right_arm.GetEndEffectorTransform())Lets return to our previous example of selecting a pose for the end-effector to allow a manipulator to grasp a glass. The following code shows the python commands that allow the TSR to be defined:
ipython> glass = env.GetKinBody('plastic_glass')
ipython> T0_w = glass.GetTransform() # We use the glass's coordinate frame as the w frame
# Now define Tw_e to represent the pose of the end-effector relative to the glass
ipython> Tw_e = numpy.array([[ 0., 0., 1., -0.20], # desired offset between end-effector and object along x-axis
[1., 0., 0., 0.],
[0., 1., 0., 0.08], # glass height
[0., 0., 0., 1.]])
ipython> Bw = numpy.zeros((6,2))
ipython> Bw[2,:] = [0.0, 0.02] # Allow a little vertical movement
ipython> Bw[5,:] = [-numpy.pi, numpy.pi] # Allow any orientation about the z-axis of the glass
ipython> robot.right_arm.SetActive() # We want to grasp with the right arm
ipython> manip_idx = robot.GetActiveManipulatorIndex()
ipython> grasp_tsr = prpy.tsr.TSR(T0_w = T0_w, Tw_e = Tw_e, Bw = Bw, manip = manip_idx)The following code shows an example of how to use a TSR to find a collision-free configuration for the manipulator that allows for a valid grasp:
ipython> ee_sample = grasp_tsr.sample() # Compute a sample pose of the end-effector
ipython> ik = robot.right_arm.FindIKSolution(ee_sample, openravepy.IkFilterOptions.CheckEnvCollisions) ik will now contain a configuration for the arm. This configuration could be given as a goal to a planner to move the robot into place for the grasp:
ipython> robot.right_arm.PlanToConfiguration(ik, execute=True)In the following code snippet, we show a method for determining whether or not the current pose of the manipulator meets the constraint by using the distance function defined on the TSR.
ipython> current_ee_pose = robot.right_arm.GetEndEffectorTransform()
ipython> dist_to_tsr = grasp_tsr.distance(current_ee_pose)
ipython> meets_constraint = (dist_to_tsr == 0.0)A single TSR, or finite set of TSRs, is sometimes insufficient to capture pose constraints of a given task. To describe more complex constraints, such as closed-chain kinematics, we can use a TSR Chain. Consider the example of opening a refrigerator door while allowing the manipulator to rotate around the handle. Here, the constraint on the motion of the hand is defined by the composition of two constraints. The first constraint describes valid locations of the handle, which all lie on the arc defined by the position of the handle relative to the door hinge. The second constraint defines the position of the robot end-effector relative to the handle. Each of these constraints can be defined by a single TSR. In order to specify the full constraint on the hand motion, we link the TSRs in a TSR Chain.
In the following code snippet, we show how to define a TSR Chain for the example of opening the refrigerator door, allowing the robot's hand to rotate around the door handle.
First we define the TSR that constrains the pose of the handle:
ipython> T0_w = hinge_pose # hinge_pose is a 4x4 matrix defining the pose of the hinge in world frame
# Now define Tw_e as the pose of the handle relative to the hinge
ipython> Tw_e = numpy.eye() # Same orientation as the hinge frame
ipython> Tw_e[0,3] = 0.4 # The handle is offset 40cm from the hinge along the x-axis of the hinge-frame
ipython> Bw = numpy.zeros((6,2)) # Assume the handle is fixed
ipython> fridge = env.GetKinBody('refridgerator')
ipython> fridge.SetActiveManipulator('door')
ipython> door_idx = fridge.GetActiveManipulatorIndex()
ipython> constraint1 = prpy.tsr.TSR(T0_w = T0_w, Tw_e = Tw_e, Bw = Bw, manip = door_idx)Next we define the TSR that constraints the pose of the hand relative to the handle:
ipython> T0_w = numpy.eye(4) # This will be ignored once we compose the chain
ipython> Tw_e = ee_in_handle # A 4x4 defining the desire pose of the end-effector relative to handle
ipython> Bw = numpy.zeros((6,2))
ipython> Bw(5,:) = [-0.25*numpy.pi, 0.25*numpy.pi]
ipython> robot.right_arm.SetActive() # use the right arm to grab the door
ipython> manip_idx = robot.GetActiveManipulatorIndex()
ipython> constraint2 = prpy.tsr.TSR(T0_w = T0_w, Tw_e = Tw_e, Bw = Bw, manip = manip_idx)Finally, we compose these into a chain:
ipython> tsrchain = prpy.tsr.TSRChain(sample_start=False, sample_goal=False, constrain=True,
TSRs = [constraint1, constraint2])Similar to the TSRs, we can sample and compute distance to chains using the sample and distance functions respectively. The sample_start, sample_goal and constrain flags will be explained in the next section.
Several of the planners in the prpy planning pipeline have support for using TSRs for defining start sets, goal sets, and trajectory-wide constraints through the PlanToTSR planning method. The method accepts as a list of TSRChain objects. The sample_start, sample_goal and constrain flags on the each TSRChain indicate to the planner how the chain should be used.
Consider the example of grasping a glass. Given our grasp_tsr we would now like to generate a plan that moves the robot to any configuration such that the end-effector meets the constraint defined by the tsr. The following code can be used to do this:
ipython> tsrchain = prpy.tsr.TSRChain(sample_goal=True, sample_start=False, constrain=False,
TSR=grasp_tsr)Defining sample_goal=True tells the planner to apply the constraint only to the last point in the plan. Now we can call the planner:
ipython> traj = robot.PlanToTSR([tsrchain])Now imagine we wish to generate a plan that starts from any point in the grasp TSR and plans to a defined configuration, config. The following code can be used to do this:
ipython> tsrchain = prpy.tsr.TSRChain(sample_goal=False, sample_start=True, constrain=False,
TSR=grasp_tsr)Defining sample_start=True tells the planner to apply the constraint only to the first point in the plan. Now we can call the planner:
ipython> traj = robot.PlanToTSR([tsrchain], jointgoals=[config])In the refrigerator opening example, the TSR chain defined a constraint on the motion of the end-effector that should be applied over the whole trajectory. We defined:
ipython> tsrchain = prpy.tsr.TSRChain(sample_start=False, sample_goal=False, constrain=True,
TSRs=[constraint1, constraint2])Here constrain=True tells the planner to apply the constraint to every point in the plan. Again, we can call the planner:
ipython> traj = robot.PlanToTSR([tsrchain], jointgoals=[config])Here, the caller must be careful to ensure that config meets the constraint defined by the TSR.
Now imagine we had to TSRs, grasp1_tsr and grasp2_tsr the each defined a set of valid configurations for grasping. We can ask the planner to generate a plan to any configuration that meets either the grasp1_tsr or the grasp2_tsr constraint in the following way:
ipython> tsrchain1 = prpy.tsr.TSRChain(sample_goal=True, sample_start=False, constrain=False,
TSR=grasp1_tsr)
ipython> tsrchain2 = prpy.tsr.TSRChain(sample_goal=True, sample_start=False, constrain=False,
TSR=grasp2_tsr)
ipython> traj = robot.PlanToTSR([tsrchain1, tsrchain2])The prpy framework contains the ability to define and cache TSRChains that are commonly used by the robot. These pre-defined TSRChains can be accessed via the tsrlibrary defined on the robot. The following shows an example for how the TSR Library might be used:
ipython> glass = env.GetKinBody('plastic_glass')
ipython> tsrlist = robot.tsrlibrary(glass, 'grasp')
ipython> traj = robot.PlanToTSR(tsrlist)The TSR library always returns a list of TSRChain objects. The return value can be passed directly to PlanToTSR.
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