Principle:ARISE Initiative Robosuite Placement Sampling
| Knowledge Sources | |
|---|---|
| Domains | Robotics, Randomization, Task Design |
| Last Updated | 2026-02-15 07:00 GMT |
Overview
A set of placement strategies for positioning simulation objects within workspace regions using randomized sampling with collision avoidance, boundary enforcement, and configurable rotation ranges.
Description
In simulation-based robot learning, objects must be placed in varied configurations at the start of each episode to provide training diversity. Simply placing objects at fixed positions produces a narrow distribution that fails to generalize, while placing them completely at random risks physically impossible configurations (overlapping objects, objects outside the workspace). Placement samplers provide a principled middle ground: they sample object positions and orientations from specified distributions while enforcing physical validity constraints.
The base placement sampler defines the interface: given a set of objects and existing fixtures in the scene, produce a dictionary mapping each object to its position and orientation. Subclasses implement specific sampling strategies. A uniform random sampler draws positions uniformly from axis-aligned rectangular regions (specified as x and y ranges relative to a reference point) and orientations from configurable rotation distributions. The sampler can optionally ensure that each object's boundary (based on its horizontal radius) stays within the sampling region, preventing objects from hanging over edges.
Collision avoidance is enforced by checking each newly sampled position against all previously placed objects and fixtures. If two objects would overlap (based on their horizontal radii), the sample is rejected and a new position is drawn. A maximum number of retries prevents infinite loops when the workspace is too crowded. Sequential composition of multiple samplers allows different objects or object groups to use different sampling strategies -- for example, placing large objects first with wider spacing, then filling in smaller objects around them.
Usage
Apply this principle when designing task environments that require randomized initial object configurations. Use uniform random samplers for general-purpose placement within rectangular regions. Compose multiple samplers sequentially when different objects have different placement requirements. Configure rotation ranges to match task semantics (e.g., upright-only for cups, any rotation for blocks).
Theoretical Basis
Uniform random sampling within bounded regions:
x = uniform(x_min, x_max) + reference_x
y = uniform(y_min, y_max) + reference_y
z = object_half_height + surface_z + z_offset
If ensure_boundary_in_range:
x = uniform(x_min + radius, x_max - radius) + reference_x
y = uniform(y_min + radius, y_max - radius) + reference_y
Rotation sampling supports multiple modes:
rotation = None: theta = uniform(0, 2*pi)
rotation = (a, b): theta = uniform(a, b)
rotation = value: theta = value (fixed)
orientation = quat_multiply(
axis_angle_to_quat(rotation_axis, theta),
object_default_quat
)
Collision avoidance via pairwise distance checking:
for each new_object:
for attempt in range(max_retries):
pos = sample_position()
valid = True
for existing_object in placed_objects:
dist = ||pos[:2] - existing_pos[:2]||
min_dist = new_radius + existing_radius
if dist < min_dist:
valid = False
break
if valid:
place(new_object, pos)
break
else:
raise RandomizationError("Could not place object")
Sequential composition enables layered placement:
fixtures = {}
fixtures = sampler_1.sample(fixtures) # place first group
fixtures = sampler_2.sample(fixtures) # place second group, avoiding first
fixtures = sampler_3.sample(fixtures) # place third group, avoiding both
Each sampler receives the accumulated fixtures and must avoid them when placing new objects.