import collections from copy import copy import numpy as np from robosuite.models.objects import MujocoObject from robosuite.utils import RandomizationError from robosuite.utils.transform_utils import quat_multiply class ObjectPositionSampler: """ Base class of object placement sampler. Args: name (str): Name of this sampler. mujoco_objects (None or MujocoObject or list of MujocoObject): single model or list of MJCF object models ensure_object_boundary_in_range (bool): If True, will ensure that the object is enclosed within a given boundary (should be implemented by subclass) ensure_valid_placement (bool): If True, will check for correct (valid) object placements reference_pos (3-array): global (x,y,z) position relative to which sampling will occur z_offset (float): Add a small z-offset to placements. This is useful for fixed objects that do not move (i.e. no free joint) to place them above the table. """ def __init__( self, name, mujoco_objects=None, ensure_object_boundary_in_range=True, ensure_valid_placement=True, reference_pos=(0, 0, 0), z_offset=0.0, ): # Setup attributes self.name = name if mujoco_objects is None: self.mujoco_objects = [] else: # Shallow copy the list so we don't modify the inputted list but still keep the object references self.mujoco_objects = [mujoco_objects] if isinstance(mujoco_objects, MujocoObject) else copy(mujoco_objects) self.ensure_object_boundary_in_range = ensure_object_boundary_in_range self.ensure_valid_placement = ensure_valid_placement self.reference_pos = reference_pos self.z_offset = z_offset def add_objects(self, mujoco_objects): """ Add additional objects to this sampler. Checks to make sure there's no identical objects already stored. Args: mujoco_objects (MujocoObject or list of MujocoObject): single model or list of MJCF object models """ mujoco_objects = [mujoco_objects] if isinstance(mujoco_objects, MujocoObject) else mujoco_objects for obj in mujoco_objects: assert obj not in self.mujoco_objects, "Object '{}' already in sampler!".format(obj.name) self.mujoco_objects.append(obj) def reset(self): """ Resets this sampler. Removes all mujoco objects from this sampler. """ self.mujoco_objects = [] def sample(self, fixtures=None, reference=None, on_top=True): """ Uniformly sample on a surface (not necessarily table surface). Args: fixtures (dict): dictionary of current object placements in the scene as well as any other relevant obstacles that should not be in contact with newly sampled objects. Used to make sure newly generated placements are valid. Should be object names mapped to (pos, quat, MujocoObject) reference (str or 3-tuple or None): if provided, sample relative placement. Can either be a string, which corresponds to an existing object found in @fixtures, or a direct (x,y,z) value. If None, will sample relative to this sampler's `'reference_pos'` value. on_top (bool): if True, sample placement on top of the reference object. Return: dict: dictionary of all object placements, mapping object_names to (pos, quat, obj), including the placements specified in @fixtures. Note quat is in (w,x,y,z) form """ raise NotImplementedError class UniformRandomSampler(ObjectPositionSampler): """ Places all objects within the table uniformly random. Args: name (str): Name of this sampler. mujoco_objects (None or MujocoObject or list of MujocoObject): single model or list of MJCF object models x_range (2-array of float): Specify the (min, max) relative x_range used to uniformly place objects y_range (2-array of float): Specify the (min, max) relative y_range used to uniformly place objects rotation (None or float or Iterable): :`None`: Add uniform random random rotation :`Iterable (a,b)`: Uniformly randomize rotation angle between a and b (in radians) :`value`: Add fixed angle rotation rotation_axis (str): Can be 'x', 'y', or 'z'. Axis about which to apply the requested rotation ensure_object_boundary_in_range (bool): :`True`: The center of object is at position: [uniform(min x_range + radius, max x_range - radius)], [uniform(min x_range + radius, max x_range - radius)] :`False`: [uniform(min x_range, max x_range)], [uniform(min x_range, max x_range)] ensure_valid_placement (bool): If True, will check for correct (valid) object placements reference_pos (3-array): global (x,y,z) position relative to which sampling will occur z_offset (float): Add a small z-offset to placements. This is useful for fixed objects that do not move (i.e. no free joint) to place them above the table. """ def __init__( self, name, mujoco_objects=None, x_range=(0, 0), y_range=(0, 0), rotation=None, rotation_axis="z", ensure_object_boundary_in_range=True, ensure_valid_placement=True, reference_pos=(0, 0, 0), z_offset=0.0, ): self.x_range = x_range self.y_range = y_range self.rotation = rotation self.rotation_axis = rotation_axis super().__init__( name=name, mujoco_objects=mujoco_objects, ensure_object_boundary_in_range=ensure_object_boundary_in_range, ensure_valid_placement=ensure_valid_placement, reference_pos=reference_pos, z_offset=z_offset, ) def _sample_x(self, object_horizontal_radius): """ Samples the x location for a given object Args: object_horizontal_radius (float): Radius of the object currently being sampled for Returns: float: sampled x position """ minimum, maximum = self.x_range if self.ensure_object_boundary_in_range: minimum += object_horizontal_radius maximum -= object_horizontal_radius return np.random.uniform(high=maximum, low=minimum) def _sample_y(self, object_horizontal_radius): """ Samples the y location for a given object Args: object_horizontal_radius (float): Radius of the object currently being sampled for Returns: float: sampled y position """ minimum, maximum = self.y_range if self.ensure_object_boundary_in_range: minimum += object_horizontal_radius maximum -= object_horizontal_radius return np.random.uniform(high=maximum, low=minimum) def _sample_quat(self): """ Samples the orientation for a given object Returns: np.array: sampled object quaternion in (w,x,y,z) form Raises: ValueError: [Invalid rotation axis] """ if self.rotation is None: rot_angle = np.random.uniform(high=2 * np.pi, low=0) elif isinstance(self.rotation, collections.abc.Iterable): rot_angle = np.random.uniform(high=max(self.rotation), low=min(self.rotation)) else: rot_angle = self.rotation # Return angle based on axis requested if self.rotation_axis == "x": return np.array([np.cos(rot_angle / 2), np.sin(rot_angle / 2), 0, 0]) elif self.rotation_axis == "y": return np.array([np.cos(rot_angle / 2), 0, np.sin(rot_angle / 2), 0]) elif self.rotation_axis == "z": return np.array([np.cos(rot_angle / 2), 0, 0, np.sin(rot_angle / 2)]) else: # Invalid axis specified, raise error raise ValueError( "Invalid rotation axis specified. Must be 'x', 'y', or 'z'. Got: {}".format(self.rotation_axis) ) def sample(self, fixtures=None, reference=None, on_top=True): """ Uniformly sample relative to this sampler's reference_pos or @reference (if specified). Args: fixtures (dict): dictionary of current object placements in the scene as well as any other relevant obstacles that should not be in contact with newly sampled objects. Used to make sure newly generated placements are valid. Should be object names mapped to (pos, quat, MujocoObject) reference (str or 3-tuple or None): if provided, sample relative placement. Can either be a string, which corresponds to an existing object found in @fixtures, or a direct (x,y,z) value. If None, will sample relative to this sampler's `'reference_pos'` value. on_top (bool): if True, sample placement on top of the reference object. This corresponds to a sampled z-offset of the current sampled object's bottom_offset + the reference object's top_offset (if specified) Return: dict: dictionary of all object placements, mapping object_names to (pos, quat, obj), including the placements specified in @fixtures. Note quat is in (w,x,y,z) form Raises: RandomizationError: [Cannot place all objects] AssertionError: [Reference object name does not exist, invalid inputs] """ # Standardize inputs placed_objects = {} if fixtures is None else copy(fixtures) if reference is None: base_offset = self.reference_pos elif type(reference) is str: assert ( reference in placed_objects ), "Invalid reference received. Current options are: {}, requested: {}".format( placed_objects.keys(), reference ) ref_pos, _, ref_obj = placed_objects[reference] base_offset = np.array(ref_pos) if on_top: base_offset += np.array((0, 0, ref_obj.top_offset[-1])) else: base_offset = np.array(reference) assert ( base_offset.shape[0] == 3 ), "Invalid reference received. Should be (x,y,z) 3-tuple, but got: {}".format(base_offset) # Sample pos and quat for all objects assigned to this sampler for obj in self.mujoco_objects: # First make sure the currently sampled object hasn't already been sampled assert obj.name not in placed_objects, "Object '{}' has already been sampled!".format(obj.name) horizontal_radius = obj.horizontal_radius bottom_offset = obj.bottom_offset success = False for i in range(5000): # 5000 retries object_x = self._sample_x(horizontal_radius) + base_offset[0] object_y = self._sample_y(horizontal_radius) + base_offset[1] object_z = self.z_offset + base_offset[2] if on_top: object_z -= bottom_offset[-1] # objects cannot overlap location_valid = True if self.ensure_valid_placement: for (x, y, z), _, other_obj in placed_objects.values(): if ( np.linalg.norm((object_x - x, object_y - y)) <= other_obj.horizontal_radius + horizontal_radius ) and (object_z - z <= other_obj.top_offset[-1] - bottom_offset[-1]): location_valid = False break if location_valid: # random rotation quat = self._sample_quat() # multiply this quat by the object's initial rotation if it has the attribute specified if hasattr(obj, "init_quat"): quat = quat_multiply(quat, obj.init_quat) # location is valid, put the object down pos = (object_x, object_y, object_z) placed_objects[obj.name] = (pos, quat, obj) success = True break if not success: raise RandomizationError("Cannot place all objects ):") return placed_objects class SequentialCompositeSampler(ObjectPositionSampler): """ Samples position for each object sequentially. Allows chaining multiple placement initializers together - so that object locations can be sampled on top of other objects or relative to other object placements. Args: name (str): Name of this sampler. """ def __init__(self, name): # Samplers / args will be filled in later self.samplers = collections.OrderedDict() self.sample_args = collections.OrderedDict() super().__init__(name=name) def append_sampler(self, sampler, sample_args=None): """ Adds a new placement initializer with corresponding @sampler and arguments Args: sampler (ObjectPositionSampler): sampler to add sample_args (None or dict): If specified, should be additional arguments to pass to @sampler's sample() call. Should map corresponding sampler's arguments to values (excluding @fixtures argument) Raises: AssertionError: [Object name in samplers] """ # Verify that all added mujoco objects haven't already been added, and add to this sampler's objects dict for obj in sampler.mujoco_objects: assert obj not in self.mujoco_objects, f"Object '{obj.name}' already has sampler associated with it!" self.mujoco_objects.append(obj) self.samplers[sampler.name] = sampler self.sample_args[sampler.name] = sample_args def hide(self, mujoco_objects): """ Helper method to remove an object from the workspace. Args: mujoco_objects (MujocoObject or list of MujocoObject): Object(s) to hide """ sampler = UniformRandomSampler( name="HideSampler", mujoco_objects=mujoco_objects, x_range=[-10, -20], y_range=[-10, -20], rotation=[0, 0], rotation_axis="z", z_offset=10, ensure_object_boundary_in_range=False, ensure_valid_placement=False, ) self.append_sampler(sampler=sampler) def add_objects(self, mujoco_objects): """ Override super method to make sure user doesn't call this (all objects should implicitly belong to sub-samplers) """ raise AttributeError("add_objects() should not be called for SequentialCompsiteSamplers!") def add_objects_to_sampler(self, sampler_name, mujoco_objects): """ Adds specified @mujoco_objects to sub-sampler with specified @sampler_name. Args: sampler_name (str): Existing sub-sampler name mujoco_objects (MujocoObject or list of MujocoObject): Object(s) to add """ # First verify that all mujoco objects haven't already been added, and add to this sampler's objects dict mujoco_objects = [mujoco_objects] if isinstance(mujoco_objects, MujocoObject) else mujoco_objects for obj in mujoco_objects: assert obj not in self.mujoco_objects, f"Object '{obj.name}' already has sampler associated with it!" self.mujoco_objects.append(obj) # Make sure sampler_name exists assert ( sampler_name in self.samplers.keys() ), "Invalid sub-sampler specified, valid options are: {}, " "requested: {}".format( self.samplers.keys(), sampler_name ) # Add the mujoco objects to the requested sub-sampler self.samplers[sampler_name].add_objects(mujoco_objects) def reset(self): """ Resets this sampler. In addition to base method, iterates over all sub-samplers and resets them """ super().reset() for sampler in self.samplers.values(): sampler.reset() def sample(self, fixtures=None, reference=None, on_top=True): """ Sample from each placement initializer sequentially, in the order that they were appended. Args: fixtures (dict): dictionary of current object placements in the scene as well as any other relevant obstacles that should not be in contact with newly sampled objects. Used to make sure newly generated placements are valid. Should be object names mapped to (pos, quat, MujocoObject) reference (str or 3-tuple or None): if provided, sample relative placement. This will override each sampler's @reference argument if not already specified. Can either be a string, which corresponds to an existing object found in @fixtures, or a direct (x,y,z) value. If None, will sample relative to this sampler's `'reference_pos'` value. on_top (bool): if True, sample placement on top of the reference object. This will override each sampler's @on_top argument if not already specified. This corresponds to a sampled z-offset of the current sampled object's bottom_offset + the reference object's top_offset (if specified) Return: dict: dictionary of all object placements, mapping object_names to (pos, quat, obj), including the placements specified in @fixtures. Note quat is in (w,x,y,z) form Raises: RandomizationError: [Cannot place all objects] """ # Standardize inputs placed_objects = {} if fixtures is None else copy(fixtures) # Iterate through all samplers to sample for sampler, s_args in zip(self.samplers.values(), self.sample_args.values()): # Pre-process sampler args if s_args is None: s_args = {} for arg_name, arg in zip(("reference", "on_top"), (reference, on_top)): if arg_name not in s_args: s_args[arg_name] = arg # Run sampler new_placements = sampler.sample(fixtures=placed_objects, **s_args) # Update placements placed_objects.update(new_placements) return placed_objects