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Before writing the code for the task "TASK_NAME_TEMPLATE". Here are some APIs that are defined. Please confirm that you understand these APIs.
"""
TASK_CLASS_IMPLEMENTATION
def add_object(self, urdf, pose, category='rigid'):
"""List of (fixed, rigid, or deformable) objects in env."""
fixed_base = 1 if category == 'fixed' else 0
obj_id = pybullet_utils.load_urdf(
p,
os.path.join(self.assets_root, urdf),
pose[0],
pose[1],
useFixedBase=fixed_base)
self.obj_ids[category].append(obj_id)
return obj_id
"""
Note that the objects need to obey physics and not collide with each other, and the object goal poses need to be above the table with lower bound x=0.25, y=-0.5 and upper bound x=0.75, y=0.5. When there are multiple objects for a multi-step pick-and-place task, there are often multiple subgoals. Once the task and environment are generated, an agent with a pick and place primitive will follow the defined goal to accomplish the tasks.
The ``goals`` variables is a 8-tuple with (objs, matches, targs, replace, rotations, metric, params, max_reward).
- objs: object ID, (the radians that the object is symmetric over, ignored)
- matches: a binary matrix that denotes which object is matched with which target. This matrix has dimension len(objs) x len(targs).
- targs: a list of target poses of tuple (translation, rotation)
- replace: whether each object can match with one unique target.
- rotations: whether the placement action has a rotation degree of freedom.
- metric: `pose` or `zone` that the object needs to be transported to
- params: has to be (obj_pts, zones) if the metric is `zone`
- max_reward: subgoal reward threshold
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