Valkyrie

CONTRIBUTORS.md

@@ -16,6 +16,7 @@ Guidelines for modifications:
 * ETH Zurich
 * NVIDIA Corporation & Affiliates
 * University of Toronto
+* Persona AI, Inc.
 
 ---
 

docs/source/overview/environments.rst

@@ -288,6 +288,8 @@ Environments based on legged locomotion tasks.
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
     | |velocity-rough-g1|          | |velocity-rough-g1-link|                     | Track a velocity command on rough terrain with the Unitree G1 robot          |
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
+    | |velocity-flat-valkyrie|     | |velocity-flat-valkyrie-link|                | Track a velocity command on flat terrain with the Valkyrie robot             |
+    +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
 
 .. |velocity-flat-anymal-b-link| replace:: `Isaac-Velocity-Flat-Anymal-B-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/flat_env_cfg.py>`__
 .. |velocity-rough-anymal-b-link| replace:: `Isaac-Velocity-Rough-Anymal-B-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_b/rough_env_cfg.py>`__
@@ -318,6 +320,8 @@ Environments based on legged locomotion tasks.
 .. |velocity-flat-g1-link| replace:: `Isaac-Velocity-Flat-G1-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/flat_env_cfg.py>`__
 .. |velocity-rough-g1-link| replace:: `Isaac-Velocity-Rough-G1-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/rough_env_cfg.py>`__
 
+.. |velocity-flat-valkyrie-link| replace:: `Isaac-Velocity-Flat-Valkyrie-v0 <https://github.com/isaac-sim/IsaacLab/blob/main/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/valkyrie/flat_env_cfg.py>`__
+
 
 .. |velocity-flat-anymal-b| image:: ../_static/tasks/locomotion/anymal_b_flat.jpg
 .. |velocity-rough-anymal-b| image:: ../_static/tasks/locomotion/anymal_b_rough.jpg
@@ -336,6 +340,7 @@ Environments based on legged locomotion tasks.
 .. |velocity-rough-h1| image:: ../_static/tasks/locomotion/h1_rough.jpg
 .. |velocity-flat-g1| image:: ../_static/tasks/locomotion/g1_flat.jpg
 .. |velocity-rough-g1| image:: ../_static/tasks/locomotion/g1_rough.jpg
+.. |velocity-flat-valkyrie| image:: ../_static/tasks/locomotion/valkyrie_flat.jpg
 
 Navigation
 ~~~~~~~~~~

source/isaaclab_assets/isaaclab_assets/robots/__init__.py

@@ -24,3 +24,4 @@
 from .spot import *
 from .unitree import *
 from .universal_robots import *
+from .valkyrie import *

source/isaaclab_assets/isaaclab_assets/robots/valkyrie.py

@@ -0,0 +1,116 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Configuration for the Valkyrie robot from IHMC Robotics.
+"""
+
+import isaaclab.sim as sim_utils
+from isaaclab.actuators import ImplicitActuatorCfg
+from isaaclab.assets.articulation import ArticulationCfg
+from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR
+
+spawn_usd = sim_utils.UsdFileCfg(
+    usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Valkyrie/valkyrie.usd",
+    activate_contact_sensors=True,
+    rigid_props=sim_utils.RigidBodyPropertiesCfg(
+        disable_gravity=False,
+        retain_accelerations=False,
+        linear_damping=0.0,
+        angular_damping=0.0,
+        max_linear_velocity=1000.0,
+        max_angular_velocity=1000.0,
+        max_depenetration_velocity=1.0,
+    ),
+    articulation_props=sim_utils.ArticulationRootPropertiesCfg(
+        enabled_self_collisions=False, solver_position_iteration_count=8, solver_velocity_iteration_count=4
+    ),
+)
+
+VALKYRIE_CFG = ArticulationCfg(
+    spawn=spawn_usd,
+    init_state=ArticulationCfg.InitialStateCfg(
+        pos=(0.0, 0.0, 1.2),
+        joint_pos={
+            ".*HipPitch": -0.20,
+            ".*KneePitch": 0.42,
+            ".*AnklePitch": -0.23,
+            "leftShoulderPitch": 0.27,
+            "rightShoulderPitch": 0.27,
+            "leftElbowPitch": -0.7,
+            "rightElbowPitch": 0.7,
+            "leftShoulderRoll": -1.2,
+            "rightShoulderRoll": 1.2,
+        },
+        joint_vel={".*": 0.0},
+    ),
+    soft_joint_pos_limit_factor=0.9,
+    actuators={
+        "torso": ImplicitActuatorCfg(
+            effort_limit=2000,
+            joint_names_expr=["torso.*"],
+            stiffness={
+                "torsoPitch": 150.0,
+                "torsoRoll": 150.0,
+                "torsoYaw": 150.0,
+            },
+            damping={
+                "torsoPitch": 5.0,
+                "torsoRoll": 5.0,
+                "torsoYaw": 5.0,
+            },
+            armature=0.01,
+        ),
+        "legs": ImplicitActuatorCfg(
+            joint_names_expr=[
+                ".*HipYaw",
+                ".*HipRoll",
+                ".*HipPitch",
+                ".*Knee.*",
+            ],
+            effort_limit=300,
+            velocity_limit=100.0,
+            stiffness={
+                ".*HipYaw": 150.0,
+                ".*HipRoll": 150.0,
+                ".*HipPitch": 200.0,
+                ".*Knee.*": 200.0,
+            },
+            damping={
+                ".*HipYaw": 5.0,
+                ".*HipRoll": 5.0,
+                ".*HipPitch": 5.0,
+                ".*Knee.*": 5.0,
+            },
+            armature={
+                ".*Hip.*": 0.01,
+                ".*Knee.*": 0.01,
+            },
+        ),
+        "feet": ImplicitActuatorCfg(
+            effort_limit=20,
+            joint_names_expr=[".*AnklePitch", ".*AnkleRoll"],
+            stiffness=20.0,
+            damping=2.0,
+            armature=0.01,
+        ),
+        "arms": ImplicitActuatorCfg(
+            joint_names_expr=[
+                ".*Shoulder.*",
+                ".*Elbow.*",
+                ".*Forearm.*",
+            ],
+            effort_limit=300,
+            velocity_limit=100.0,
+            stiffness=40.0,
+            damping=10.0,
+            armature={
+                ".*Shoulder.*": 0.01,
+                ".*Elbow.*": 0.01,
+                ".*Forearm.*": 0.001,
+            },
+        ),
+    },
+)

source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/g1/rough_env_cfg.py

@@ -47,7 +47,7 @@ class G1Rewards(RewardsCfg):
         },
     )
 
-    # Penalize ankle joint limits
+    # PenalizIsaac-Velocity-Flat-Valkyrie-v0e ankle joint limits
     dof_pos_limits = RewTerm(
         func=mdp.joint_pos_limits,
         weight=-1.0,

source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/valkyrie/__init__.py

@@ -0,0 +1,35 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+import gymnasium as gym
+
+from . import agents, flat_env_cfg, rough_env_cfg
+
+##
+# Register Gym environments.
+##
+
+gym.register(
+    id="Isaac-Velocity-Flat-Valkyrie-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": flat_env_cfg.ValkyrieFlatEnvCfg,
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:ValkyrieFlatPPORunnerCfg",
+        "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml",
+    },
+)
+
+
+gym.register(
+    id="Isaac-Velocity-Flat-Valkyrie-Play-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": flat_env_cfg.ValkyrieFlatEnvCfg_PLAY,
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:ValkyrieFlatPPORunnerCfg",
+        "skrl_cfg_entry_point": f"{agents.__name__}:skrl_flat_ppo_cfg.yaml",
+    },
+)

source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/valkyrie/agents/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause

source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/valkyrie/agents/skrl_flat_ppo_cfg.yaml

@@ -0,0 +1,82 @@
+seed: 42
+
+
+# Models are instantiated using skrl's model instantiator utility
+# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html
+models:
+  separate: False
+  policy:  # see gaussian_model parameters
+    class: GaussianMixin
+    clip_actions: False
+    clip_log_std: True
+    min_log_std: -20.0
+    max_log_std: 2.0
+    initial_log_std: 0.0
+    network:
+      - name: net
+        input: STATES
+        layers: [256, 128, 128]
+        activations: elu
+    output: ACTIONS
+  value:  # see deterministic_model parameters
+    class: DeterministicMixin
+    clip_actions: False
+    network:
+      - name: net
+        input: STATES
+        layers: [256, 128, 128]
+        activations: elu
+    output: ONE
+
+
+# Rollout memory
+# https://skrl.readthedocs.io/en/latest/api/memories/random.html
+memory:
+  class: RandomMemory
+  memory_size: -1  # automatically determined (same as agent:rollouts)
+
+
+# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG)
+# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html
+agent:
+  class: PPO
+  # The agent collects <rollouts> number of environment steps during each episode.
+  # The collected data is then divided into <mini_batches> and used for <learning_epochs> optimization epochs.
+  rollouts: 24 # number of steps to take in each rollout
+  learning_epochs: 5 #
+  mini_batches: 4     # number of mini baches
+  discount_factor: 0.99
+  lambda: 0.95
+  learning_rate: 1.0e-03
+  learning_rate_scheduler: KLAdaptiveLR
+  learning_rate_scheduler_kwargs:
+    kl_threshold: 0.01
+  state_preprocessor: null
+  state_preprocessor_kwargs: null
+  value_preprocessor: null
+  value_preprocessor_kwargs: null
+  random_timesteps: 0
+  learning_starts: 0
+  grad_norm_clip: 1.0
+  ratio_clip: 0.2 # clipping coefficient for computing the clipped surrogate objective
+  value_clip: 0.2 # clipping coefficient for computing the value loss (if clip_predicted_values is True)
+  clip_predicted_values: True # clip predicted values during value loss computation
+  entropy_loss_scale: 0.008 # 0.008 # the bigger, the more exploratin
+  value_loss_scale: 1.0
+  kl_threshold: 0.0
+  rewards_shaper_scale: 1.0
+  time_limit_bootstrap: False
+  # logging and checkpoint
+  experiment:
+    directory: "valk_flat"
+    experiment_name: ""
+    write_interval: auto
+    checkpoint_interval: auto
+
+
+# Sequential trainer
+# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html
+trainer:
+  class: SequentialTrainer
+  timesteps: 36000
+  environment_info: log

source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/valkyrie/flat_env_cfg.py

@@ -0,0 +1,87 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.utils import configclass
+
+from .rough_env_cfg import ValkyrieRoughEnvCfg
+
+
+@configclass
+class ValkyrieFlatEnvCfg(ValkyrieRoughEnvCfg):
+    def __post_init__(self):
+        # post init of parent
+        super().__post_init__()
+
+        # change terrain to flat
+        self.scene.terrain.terrain_type = "plane"
+        self.scene.terrain.terrain_generator = None
+        # no height scan
+        self.scene.height_scanner = None
+        self.observations.policy.height_scan = None
+        # no terrain curriculum
+        self.curriculum.terrain_levels = None
+
+        # Rewards
+        self.rewards.track_lin_vel_xy_exp.weight = 2.0
+        self.rewards.track_ang_vel_z_exp.weight = 1.0
+        self.rewards.flat_orientation_l2.weight = -1.0
+
+        self.rewards.lin_vel_z_l2.weight = -0.2
+        self.rewards.action_rate_l2.weight = -0.005
+
+        self.rewards.feet_air_time.weight = 2.5
+        self.rewards.feet_air_time.params["threshold"] = 0.4
+
+        # penalize joint limit if they cross soft limit
+        self.rewards.feet_pos_limits.weight = -1.0
+        self.rewards.torso_roll_pos_limits.weight = -1.0
+
+        self.rewards.joint_deviation_hip.weight = -0.4
+        self.rewards.joint_deviation_feet.weight = -0.4
+        self.rewards.joint_deviation_arms.weight = -1.0
+        self.rewards.joint_deviation_torso.weight = -1.0
+        self.rewards.joint_deviation_torso_roll.weight = -1.0
+
+        self.rewards.feet_slide.weight = -0.1
+
+        self.rewards.dof_acc_l2.weight = -1.25e-7
+        self.rewards.dof_acc_l2.params["asset_cfg"] = SceneEntityCfg("robot", joint_names=[".*Hip.*", ".*Knee.*"])
+        self.rewards.dof_torques_l2.weight = -1.5e-7
+        self.rewards.dof_torques_l2.params["asset_cfg"] = SceneEntityCfg("robot", joint_names=[".*Hip.*", ".*Knee.*"])
+        # Commands
+        self.commands.base_velocity.ranges.lin_vel_x = (0.0, 1.0)
+        self.commands.base_velocity.ranges.lin_vel_y = (-0.5, 0.5)
+        self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0)
+
+
+class ValkyrieFlatEnvCfg_PLAY(ValkyrieFlatEnvCfg):
+    def __post_init__(self) -> None:
+        # post init of parent
+        super().__post_init__()
+
+        # make a smaller scene for play
+        self.scene.num_envs = 50
+        self.scene.env_spacing = 2.5
+        # disable randomization for play
+        self.observations.policy.enable_corruption = False
+        # remove random pushing
+        self.events.base_external_force_torque = None
+        self.events.push_robot = None
+
+        self.commands.base_velocity.ranges.lin_vel_x = (0.0, 1.0)
+        self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0)
+        self.commands.base_velocity.ranges.ang_vel_z = (0.0, 0.0)
+        self.events.reset_base.params = {
+            "pose_range": {"x": (0, 0), "y": (0, 0), "yaw": (0, 0)},
+            "velocity_range": {
+                "x": (0.0, 0.0),
+                "y": (0.0, 0.0),
+                "z": (0.0, 0.0),
+                "roll": (0.0, 0.0),
+                "pitch": (0.0, 0.0),
+                "yaw": (0.0, 0.0),
+            },
+        }