isaac-sim · hujc7 · Apr 16, 2026 · Apr 22, 2026 · Apr 22, 2026 · isaaclab-review-bot
diff --git a/source/isaaclab_tasks/config/extension.toml b/source/isaaclab_tasks/config/extension.toml
@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "1.5.28"
+version = "1.5.30"
 
 # Description
 title = "Isaac Lab Environments"

diff --git a/source/isaaclab_tasks/docs/CHANGELOG.rst b/source/isaaclab_tasks/docs/CHANGELOG.rst
@@ -1,6 +1,39 @@
 Changelog
 ---------
 
+1.5.30 (2026-04-27)
+~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added Newton rough terrain support for the G1 biped locomotion velocity
+  env. The only engine-specific change is a ~1.7x ``max_iterations`` preset on
+  :class:`~isaaclab_tasks.manager_based.locomotion.velocity.config.g1.agents.rsl_rl_ppo_cfg.G1RoughPPORunnerCfg`
+  (Newton = 5000, PhysX = 3000). PhysX saturates near iter 3000 on both
+  reward (≈ +18) and episode length (≈ 980) and does not meaningfully
+  improve further; Newton reaches the same (reward, ep_len) quality at
+  iter 5000. The iteration budget is bumped rather than tuning physics
+  or reward terms.
+
+
+1.5.29 (2026-04-27)
+~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Re-enabled ``add_base_mass`` randomization on H1 and Cassie in their
+  rough-terrain configs (previously ``= None`` per the pre-existing biped
+  convention). H1 uses the shared log-uniform scale default from
+  ``EventsCfg``; Cassie overrides to ``(1.0, 1.25)`` asymmetric heavier-bias
+  (never lighter than nominal). Symmetric ±25% regressed Cassie reward by
+  40% vs disabled due to closed-loop Achilles coupling destabilizing on
+  lighter pelvis mass; ``(1.0, 1.25)`` recovers to 90% of the
+  mass-rand-disabled baseline while retaining the domain-randomization
+  benefit.
+
+
 1.5.28 (2026-04-24)
 ~~~~~~~~~~~~~~~~~~~
 

@@ -13,6 +13,7 @@
     LocomotionVelocityRoughEnvCfg,
     RewardsCfg,
 )
+from isaaclab_tasks.utils import preset
 
 ##
 # Pre-defined configs
@@ -60,12 +61,23 @@ def __post_init__(self):
         super().__post_init__()
         # scene
         self.scene.robot = CASSIE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
+        # Cassie Newton-only armature for biped stability on rough terrain; PhysX unchanged
+        self.scene.robot.actuators["legs"].armature = preset(default=0.0, newton=0.02)
+
         self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/pelvis"
 
-        # Cassie uses "pelvis" as base body — disable mass randomization for bipeds
-        self.events.add_base_mass = None
+        # Cassie uses "pelvis" as base body. Override the shared symmetric
+        # (1/1.25, 1.25) log-uniform scale with asymmetric (1.0, 1.25) —
+        # lighter-than-nominal pelvis destabilizes Cassie's closed-loop
+        # Achilles coupling + hip PD response, so only heavier perturbations
+        # are safe. Symmetric ±25% regressed reward 40% vs disabled;
+        # (1.0, 1.25) recovers to 90% of baseline.
+        self.events.add_base_mass.params["asset_cfg"].body_names = "pelvis"
+        self.events.add_base_mass.params["mass_distribution_params"] = (1.0, 1.25)
         self.events.base_com = None
         self.events.base_external_force_torque.params["asset_cfg"].body_names = ".*pelvis"
+        # Cassie has precise initial pose — don't scale joint defaults randomly on reset
+        self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
 
         # actions
         self.actions.joint_pos.scale = 0.5

@@ -232,6 +232,8 @@ def __post_init__(self):
         self.events.add_base_mass.params["asset_cfg"].body_names = "torso_base"
         self.events.base_external_force_torque.params["asset_cfg"].body_names = "torso_base"
         self.events.base_com.default.params["asset_cfg"].body_names = "torso_base"
+        # Digit has precise initial pose — don't scale joint defaults randomly on reset
+        self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
 
         # Override actuator to target only actuated joints. Digit has ball joints (rod constraints)
         # that MuJoCo represents with 4 DoFs instead of 3, inflating joint_pos to 74 columns while

@@ -7,11 +7,19 @@
 
 from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg
 
+from isaaclab_tasks.utils import preset
+
 
 @configclass
 class G1RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
     num_steps_per_env = 24
-    max_iterations = 3000
+    # Newton needs ~1.7x the PPO iterations to match PhysX on G1. PhysX saturates near iter 3000
+    # (reward ≈ +18, ep_len ≈ 980) and does not meaningfully improve on either metric past that —
+    # reward oscillates +16 to +19 through iter 7500, ep_len stays flat. Newton reaches the same
+    # (reward, ep_len) quality at iter 5000 (+16 / 984). Comparing reward alone is misleading:
+    # ep_len confirms the robot is stable in both cases. The gap is sample-efficiency, not a
+    # ceiling — no physics or reward tuning closes it.
+    max_iterations = preset(default=3000, newton=5000)
     save_interval = 50
     experiment_name = "g1_rough"
     policy = RslRlPpoActorCriticCfg(

@@ -119,6 +119,8 @@ def __post_init__(self):
         self.events.add_base_mass = None
         self.events.base_com = None
         self.events.base_external_force_torque.params["asset_cfg"].body_names = "torso_link"
+        # G1 has precise initial pose — don't scale joint defaults randomly on reset
+        self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
 
         # Rewards
         self.rewards.lin_vel_z_l2.weight = 0.0

@@ -83,8 +83,11 @@ def __post_init__(self):
         if self.scene.height_scanner:
             self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/torso_link"
 
-        # H1 uses "torso_link" as base body — disable mass randomization for bipeds
-        self.events.add_base_mass = None
+        # H1 uses "torso_link" as base body; inherits the shared log-uniform mass
+        # randomization scale from EventsCfg (no per-H1 override needed).
+        self.events.add_base_mass.params["asset_cfg"].body_names = "torso_link"
+        # H1 has precise initial pose — don't scale joint defaults randomly on reset
+        self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
         self.events.base_com = None
         self.events.base_external_force_torque.params["asset_cfg"].body_names = ".*torso_link"