diff --git a/igibson/configs/behavior_robot_vr_behavior_task.yaml b/igibson/configs/behavior_robot_vr_behavior_task.yaml
index aaf53e2e0..182efc394 100644
--- a/igibson/configs/behavior_robot_vr_behavior_task.yaml
+++ b/igibson/configs/behavior_robot_vr_behavior_task.yaml
@@ -18,6 +18,7 @@ robot:
   # For the VR use case, we use the higher set of joint speed/position limits, and a different gripper action space.
   show_visual_head: False
   higher_limits: True
+  legacy_proprioception: True
   controller_config:
     gripper_right_hand:
       name: JointController
diff --git a/igibson/robots/behavior_robot.py b/igibson/robots/behavior_robot.py
index a30c121e3..12e22cc58 100644
--- a/igibson/robots/behavior_robot.py
+++ b/igibson/robots/behavior_robot.py
@@ -108,6 +108,7 @@ def __init__(
         base_name="body",
         grasping_mode="assisted",
         higher_limits=False,
+        legacy_proprioception=False,
         **kwargs,
     ):
         """
@@ -133,6 +134,8 @@ def __init__(
         :param higher_limits: bool, indicating whether the limbs' velocity and position limits should be set
             in the extended mode which allows for the instant responsiveness required for VR. Defaults to True.
             learning-based agents should set this parameter to False for a more realistic capability set.
+        :param legacy_proprioception: Use the proprioception dimensions and dict keys that the BehaviorRobot had at
+            the BEHAVIOR challenge, rather than the ones automatically generated by underlying robot classes.
         :param **kwargs: see ManipulationRobot, LocomotionRobot, ActiveCameraRobot
         """
         assert reset_joint_pos is None, "BehaviorRobot doesn't support hand-specified reset_joint_pos"
@@ -144,6 +147,7 @@ def __init__(
         self.use_ghost_hands = use_ghost_hands
         self.normal_color = normal_color
         self.show_visual_head = show_visual_head
+        self._legacy_proprioception = legacy_proprioception
 
         self._position_limit_coefficient = 1 if higher_limits else LOWER_LIMITS_POSITION_COEFFICIENT
         self._velocity_limit_coefficient = 1 if higher_limits else LOWER_LIMITS_VELOCITY_COEFFICIENT
@@ -290,6 +294,51 @@ def set_position_orientation(self, pos, orn):
 
         clear_cached_states(self)
 
+    @property
+    def default_proprio_obs(self):
+        if self._legacy_proprioception:
+            return [
+                "left_hand_position_local",
+                "left_hand_orientation_local",
+                "right_hand_position_local",
+                "right_hand_orientation_local",
+                "eye_position_local",
+                "eye_orientation_local",
+                "left_hand_trigger_fraction",
+                "left_hand_is_grasping",
+                "right_hand_trigger_fraction",
+                "right_hand_is_grasping",
+            ]
+
+        # Otherwise just return the default observations
+        return super().default_proprio_obs
+
+    def _get_proprioception_dict(self):
+        if self._legacy_proprioception:
+            # In the legacy proprioception mode, we use some custom labels and fields.
+            state = {}
+
+            # Get all the part poses first.
+            for part_name, part in self._parts.items():
+                if part_name == "body":
+                    continue
+                part_pos, part_orn = part.get_local_position_orientation()
+                state[f"{part_name}_position_local"] = part_pos
+                state[f"{part_name}_orientation_local"] = p.getEulerFromQuaternion(part_orn)
+
+            # Get grasping information.
+            for arm in self.arm_names:
+                finger_pos = self.joint_positions[self.gripper_control_idx[arm]]
+                min_pos = self.joint_lower_limits[self.gripper_control_idx[arm]]
+                max_pos = self.joint_upper_limits[self.gripper_control_idx[arm]]
+                trigger_fraction = np.max((finger_pos - min_pos) / (max_pos - min_pos))
+                state[f"{arm}_trigger_fraction"] = [np.clip(trigger_fraction, 0, 1)]
+                state[f"{arm}_is_grasping"] = [self.is_grasping(arm=arm)]
+
+            return state
+
+        return super()._get_proprioception_dict()
+
     def set_poses(self, poses):
         assert len(poses) == len(self._parts), "Number of poses (%d) does not match number of parts (%d)" % (
             len(poses),
diff --git a/tests/test_robot.py b/tests/test_robot.py
index 550b97877..a6326c14d 100644
--- a/tests/test_robot.py
+++ b/tests/test_robot.py
@@ -313,3 +313,15 @@ def test_behavior_robot_stability():
         )
         _, angle = p.getAxisAngleFromQuaternion(rel_orn)
         assert angle < np.deg2rad(5), "Part orientation moved relative to robot."
+
+
+def test_behavior_robot_legacy_proprioception():
+    s = Simulator(physics_timestep=1 / 30, render_timestep=1 / 30, mode="headless")
+    scene = EmptyScene()
+    s.import_scene(scene)
+
+    robot = REGISTERED_ROBOTS["BehaviorRobot"](legacy_proprioception=True)
+    s.import_object(robot)
+
+    proprio_length = robot.get_proprioception().shape
+    assert proprio_length == (22,), f"Proprioception shape should be 22. Instead, it is {proprio_length}"