import jax import jax.numpy as jnp from brittle_star_project.environment.obs_processing import create_obs_processor from brittle_star_project.environment.env_config import MorphMode, ObservationBoundsConfig obs_bounds = ObservationBoundsConfig().to_bounds_dict() # Features per decentralized agent (one arm's data): # disk_z_tilt → scalar → 1 feat # joint_actuator_force → 4 segs × 2 joints → 8 feat # joint_position → 4 segs × 2 joints → 8 feat # joint_velocity → 4 segs × 2 joints → 8 feat # robot_direction_to_target→ (x, y) → 2 feat # segment_contact → 4 segs → 4 feat # Total per agent: 1+8+8+8+2+4 = 31 NUM_ARMS = 5 SEGS_PER_ARM = 4 # healthy segments per arm JOINTS_PER_SEG = 2 # from _build_joint_indices: segs * 2 SEGS_HEALTHY = [4, 4, 4, 4, 4] SEGS_DAMAGED = [4, 4, 4, 4, 0] # arm 4 fully disabled SEGS_DAMAGED_2 = [4, 0, 4, 2, 4] # arm 3 fully disabled AGENT_INDICES = [0, 1, 2, 3, 4] FEAT_PER_AGENT = 1 + 8 + 8 + 8 + 2 + 4 # = 31 # Centralized flattening (needed_copies=1, one copy of global features): # disk_z_tilt → repeated once → 1 feat # joint_actuator_force → 5 arms × 8 joints → 40 feat # joint_position → 5 arms × 8 joints → 40 feat # joint_velocity → 5 arms × 8 joints → 40 feat # robot_direction_to_target→ repeated once → 2 feat # segment_contact → 5 arms × 4 segs → 20 feat # Total: 1+40+40+40+2+20 = 143 FEAT_CENTRALIZED = 1 + 40 + 40 + 40 + 2 + 20 # = 143 def make_obs(segs_per_arm: list[int]) -> dict: total_segs = sum(segs_per_arm) total_joints = JOINTS_PER_SEG * total_segs return { "actuator_force": jnp.ones(total_joints), "disk_angular_velocity": jnp.zeros(3), "disk_linear_velocity": jnp.zeros(3), "disk_position": jnp.zeros(3), "disk_rotation": jnp.array([0.1, 0.1, 0.5]), # (roll, pitch, yaw) "joint_actuator_force": jnp.full(total_joints, 1.0), "joint_position": jnp.full(total_joints, 0.5), "joint_velocity": jnp.full(total_joints, 2.0), "segment_contact": jnp.ones(total_segs), "tendon_position": jnp.zeros(0), "tendon_velocity": jnp.zeros(0), "unit_xy_direction_to_target": jnp.array([1.0, 0.0]), "xy_distance_to_target": jnp.array([3.5]), } def batch_obs(obs: dict): return jax.tree_util.tree_map(lambda x: x[None, :], obs) def make_processor(morph_mode: MorphMode, needed_copies: int, segments_per_arm: list[int]): return create_obs_processor( bounds_dict=obs_bounds, num_arms=NUM_ARMS, needed_copies=needed_copies, morph_mode=morph_mode, segments_per_arm=segments_per_arm, agent_indices=AGENT_INDICES, ) def test_centralized_no_damage(): proc = make_processor(MorphMode.CENTRALIZED, 1, SEGS_HEALTHY) obs = make_obs(SEGS_HEALTHY) obs = batch_obs(obs) global_state = proc(obs) # Centralized: 5 agents flattened into 1 → shape (1, 1, 155) assert global_state.shape == (1, 1, FEAT_CENTRALIZED) def test_centralized_damaged_1_arm(): proc = make_processor(MorphMode.CENTRALIZED, 1, SEGS_DAMAGED) obs = make_obs(SEGS_DAMAGED) obs = batch_obs(obs) global_state = proc(obs) # shape test assert global_state.shape == (1, 1, FEAT_CENTRALIZED) def test_centralized_damaged_2_arms(): proc = make_processor(MorphMode.CENTRALIZED, 1, SEGS_DAMAGED_2) obs = make_obs(SEGS_DAMAGED_2) obs = batch_obs(obs) global_state = proc(obs) # shape test assert global_state.shape == (1, 1, FEAT_CENTRALIZED) def test_decentralized_fully_connected_no_damage(): proc = make_processor(MorphMode.FULLY_CONNECTED, NUM_ARMS, SEGS_HEALTHY) obs = make_obs(SEGS_HEALTHY) obs = batch_obs(obs) global_state = proc(obs) # shape test assert global_state.shape == (1, NUM_ARMS, FEAT_PER_AGENT) def test_decentralized_fully_connected_damaged_1_arm(): proc = make_processor(MorphMode.FULLY_CONNECTED, NUM_ARMS, SEGS_DAMAGED) obs = make_obs(SEGS_DAMAGED) obs = batch_obs(obs) global_state = proc(obs) # shape test assert global_state.shape == (1, NUM_ARMS, FEAT_PER_AGENT)