diff --git a/src/openpi/configs/robots/b1k.py b/src/openpi/configs/robots/b1k.py
index 0fa8121..f6c9006 100644
--- a/src/openpi/configs/robots/b1k.py
+++ b/src/openpi/configs/robots/b1k.py
@@ -4,24 +4,26 @@ from .base_config import ObservationConfig, StateActionConfig, RobotConfig, regi
 # R1Pro Robot Configuration
 # Dual-arm mobile manipulator with base, torso, and multiple camera views
 R1Pro = RobotConfig(
-    name="robot",
+    # BEHAVIOR-1K v3.9.2 eval names the robot "robot_r1" (see OmniGibson/omnigibson/eval/r1pro.yaml),
+    # so the flattened observation keys the eval client streams are prefixed "robot_r1::".
+    name="robot_r1",
     robot_type="R1Pro",
     observations={
         "image_0": ObservationConfig(
             name="head",
-            obs_key="robot::robot:zed_link:Camera:0::rgb",
+            obs_key="robot_r1::robot_r1:zed_link:Camera:0::rgb",
             dataset_key="observation.rgb.zed_link_camera_0",
             resolution=[240, 240]
         ),
         "image_1": ObservationConfig(
             name="left_wrist",
-            obs_key="robot::robot:left_realsense_link:Camera:0::rgb",
+            obs_key="robot_r1::robot_r1:left_realsense_link:Camera:0::rgb",
             dataset_key="observation.rgb.left_realsense_link_camera_0",
             resolution=[240, 240]
         ),
         "image_2": ObservationConfig(
             name="right_wrist",
-            obs_key="robot::robot:right_realsense_link:Camera:0::rgb",
+            obs_key="robot_r1::robot_r1:right_realsense_link:Camera:0::rgb",
             dataset_key="observation.rgb.right_realsense_link_camera_0",
             resolution=[240, 240]
         ),
diff --git a/src/openpi/configs/tasks/b1k.py b/src/openpi/configs/tasks/b1k.py
index 193c4e7..e492b58 100644
--- a/src/openpi/configs/tasks/b1k.py
+++ b/src/openpi/configs/tasks/b1k.py
@@ -3,6 +3,13 @@ from . import TASK_REGISTRY
 # i3l task prompts
 TASKS = {
     "turning_on_radio": "Turn on the radio receiver that's on the table in the living room.",
+    # Task 22 in the 2026 BEHAVIOR Challenge task list. The policy was fine-tuned with
+    # prompt_from_task=True, so the training prompt was the raw LeRobot task string
+    # "putting_shoes_on_rack" (the PaliGemma tokenizer lowercases it and turns "_" into " ").
+    "putting_shoes_on_rack": "putting shoes on rack",
+    # Task 38 in the 2026 BEHAVIOR Challenge. Same convention: prompt_from_task=True during
+    # fine-tuning, so the training prompt is the raw LeRobot task string.
+    "spraying_for_bugs": "spraying for bugs",
 }
 
 # Register in global registry
diff --git a/src/openpi/training/config.py b/src/openpi/training/config.py
index ec6cd0f..7200a0b 100644
--- a/src/openpi/training/config.py
+++ b/src/openpi/training/config.py
@@ -772,6 +772,61 @@ _CONFIGS = [
         assets_base_dir="./outputs/assets",
         checkpoint_base_dir="./outputs/checkpoints",
     ),
+    # Task 38 (spraying_for_bugs) -- full fine-tune, mirror of pi05_b1k. Needs multi-GPU (FSDP)
+    # or a large single GPU; use the _lora variant below to fit on one ~46 GB card.
+    TrainConfig(
+        name="pi05_b1k_spraying_for_bugs",
+        model=pi0_config.Pi0Config(action_horizon=32, pi05=True),
+        data=LeRobotB1KDataConfig(
+            repo_id="spraying_for_bugs",
+            base_config=DataConfig(
+                data_cls=_lerobot_compat.LeRobotDataset,
+                dataset_root="/opt/b1k/datasets/spraying_for_bugs",
+                prompt_from_task=True,
+                dataset_kwargs={"tolerance_s": 5e-4},
+            ),
+            robot_config_name="b1k/R1Pro",
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+        save_interval=10_000,
+        num_train_steps=50_000,
+        assets_base_dir="./outputs/assets",
+        checkpoint_base_dir="./outputs/checkpoints",
+    ),
+    # Task 38 (spraying_for_bugs) -- LoRA fine-tune. Fits on a single L40S (~46 GB), even alongside
+    # the eval policy server. Lower ceiling than the full fine-tune above.
+    TrainConfig(
+        name="pi05_b1k_spraying_for_bugs_lora",
+        model=pi0_config.Pi0Config(
+            action_horizon=32,
+            pi05=True,
+            paligemma_variant="gemma_2b_lora",
+            action_expert_variant="gemma_300m_lora",
+        ),
+        data=LeRobotB1KDataConfig(
+            repo_id="spraying_for_bugs",
+            base_config=DataConfig(
+                data_cls=_lerobot_compat.LeRobotDataset,
+                dataset_root="/opt/b1k/datasets/spraying_for_bugs",
+                prompt_from_task=True,
+                dataset_kwargs={"tolerance_s": 5e-4},
+            ),
+            robot_config_name="b1k/R1Pro",
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+        freeze_filter=pi0_config.Pi0Config(
+            action_horizon=32,
+            pi05=True,
+            paligemma_variant="gemma_2b_lora",
+            action_expert_variant="gemma_300m_lora",
+        ).get_freeze_filter(),
+        ema_decay=None,
+        save_interval=10_000,
+        num_train_steps=50_000,
+        batch_size=16,
+        assets_base_dir="./outputs/assets",
+        checkpoint_base_dir="./outputs/checkpoints",
+    ),
     #
     # Fine-tuning Libero configs.
     #