Add walking policy for stompy2

sim/deploy/run.py

@@ -14,11 +14,11 @@
 import numpy as np
 from humanoid.envs import *
 from humanoid.utils import task_registry
-from isaacgym import gymapi
 from isaacgym.torch_utils import *
 from policy import SimPolicy
 from tqdm import tqdm
 
+from isaacgym import gymapi
 from sim.deploy.config import RobotConfig
 from sim.env import stompy_mjcf_path
 from sim.stompy.joints import StompyFixed

sim/humanoid_gym/envs/__init__.py

@@ -15,6 +15,8 @@
 from .getup_env import GetupFreeEnv
 from .legs_config import LegsCfg, LegsCfgPPO
 from .legs_env import LegsFreeEnv
+from .only_legs_config import OnlyLegsCfg, OnlyLegsCfgPPO
+from .only_legs_env import OnlyLegsFreeEnv
 from .stompy_config import StompyCfg, StompyCfgPPO
 from .stompy_env import StompyFreeEnv
 
@@ -30,6 +32,7 @@ def register_tasks() -> None:
     task_registry.register("stompy_ppo", StompyFreeEnv, StompyCfg(), StompyCfgPPO())
     task_registry.register("getup_ppo", GetupFreeEnv, GetupCfg(), GetupCfgPPO())
     task_registry.register("legs_ppo", LegsFreeEnv, LegsCfg(), LegsCfgPPO())
+    task_registry.register("only_legs_ppo", OnlyLegsFreeEnv, OnlyLegsCfg(), OnlyLegsCfgPPO())
 
 
 register_tasks()

sim/humanoid_gym/envs/getup_env.py

@@ -5,9 +5,9 @@
 from humanoid.envs import LeggedRobot
 from humanoid.envs.base.legged_robot_config import LeggedRobotCfg
 from humanoid.utils.terrain import HumanoidTerrain
-from isaacgym import gymtorch
 from isaacgym.torch_utils import *
 
+from isaacgym import gymtorch
 from sim.stompy.joints import Stompy
 
 default_feet_height = 0.0

sim/humanoid_gym/envs/legs_env.py

@@ -5,9 +5,9 @@
 from humanoid.envs import LeggedRobot
 from humanoid.envs.base.legged_robot_config import LeggedRobotCfg
 from humanoid.utils.terrain import HumanoidTerrain
-from isaacgym import gymtorch
 from isaacgym.torch_utils import *
 
+from isaacgym import gymtorch
 from sim.stompy.joints import StompyFixed
 
 

sim/humanoid_gym/envs/only_legs_config.py

@@ -0,0 +1,268 @@
+"""Defines the environment configuration for the Getting up task"""
+
+from humanoid.envs.base.legged_robot_config import (  # type: ignore
+    LeggedRobotCfg,
+    LeggedRobotCfgPPO,
+)
+
+from sim.env import stompy_urdf_path
+from sim.stompy_legs.joints import Stompy
+
+NUM_JOINTS = len(Stompy.all_joints())  # 33
+
+
+class OnlyLegsCfg(LeggedRobotCfg):
+    """
+    Configuration class for the Legs humanoid robot.
+    """
+
+    class env(LeggedRobotCfg.env):
+        # change the observation dim
+
+        frame_stack = 15
+        c_frame_stack = 3
+        num_single_obs = 11 + NUM_JOINTS * 3
+        num_observations = int(frame_stack * num_single_obs)
+        single_num_privileged_obs = 25 + NUM_JOINTS * 4
+        num_privileged_obs = int(c_frame_stack * single_num_privileged_obs)
+        num_actions = NUM_JOINTS
+        num_envs = 4096
+        episode_length_s = 24  # episode length in seconds
+        use_ref_actions = False
+
+    class safety:
+        # safety factors
+        pos_limit = .9
+        vel_limit = .9
+        torque_limit = .9
+
+    class asset(LeggedRobotCfg.asset):
+        file = str(stompy_urdf_path(legs_only=True))
+
+        name = "stompy"
+
+        foot_name = "_foot_1_rmd_x4_24_mock_1_inner_rmd_x4_24_1"
+        knee_name = "_rmd_x8_90_mock_3_inner_rmd_x8_90_1"
+
+        termination_height = 0.23
+        default_feet_height = 0.0
+        terminate_after_contacts_on = ["link_leg_assembly_left_1_leg_part_1_2", "link_leg_assembly_right_1_leg_part_1_2"]
+
+        penalize_contacts_on = []
+        self_collisions = 0 # 1 to disable, 0 to enable...bitwise filter
+
+        collapse_fixed_joints = True
+
+        flip_visual_attachments = False
+        replace_cylinder_with_capsule = False
+        fix_base_link = False
+
+    class terrain(LeggedRobotCfg.terrain):
+        mesh_type = "plane"
+        # mesh_type = 'trimesh'
+        curriculum = False
+        # rough terrain only:
+        measure_heights = False
+        static_friction = 0.6
+        dynamic_friction = 0.6
+        terrain_length = 8.0
+        terrain_width = 8.0
+        num_rows = 10  # number of terrain rows (levels)
+        num_cols = 10  # number of terrain cols (types)
+        max_init_terrain_level = 10  # starting curriculum state
+        # plane; obstacles; uniform; slope_up; slope_down, stair_up, stair_down
+        terrain_proportions = [0.2, 0.2, 0.4, 0.1, 0.1, 0, 0]
+        restitution = 0.0
+
+    class noise:
+        add_noise = False
+        noise_level = 0.6  # scales other values
+
+        class noise_scales:
+            dof_pos = 0.05
+            dof_vel = 0.5
+            ang_vel = 0.1
+            lin_vel = 0.05
+            quat = 0.03
+            height_measurements = 0.1
+
+    class init_state(LeggedRobotCfg.init_state):
+        pos = [0.0, 0.0, .72]
+
+        default_joint_angles = {k: 0.0 for k in Stompy.all_joints()}
+
+        default_positions = Stompy.default_standing()
+        for joint in default_positions:
+            default_joint_angles[joint] = default_positions[joint]
+
+    class control(LeggedRobotCfg.control):
+        # PD Drive parameters:
+        stiffness = {
+            "shoulder": 200,
+            "elbow": 200,
+            "wrist": 200,
+            "hand": 200,
+            "torso": 200,
+            "hip": 250,
+            "ankle": 15,
+            "knee": 350,
+        }
+        damping = {
+            "shoulder": 10,
+            "elbow": 10,
+            "wrist": 10,
+            "hand": 10,
+            "torso": 10,
+            "hip": 10,
+            "ankle": 10,
+            "knee": 10,
+        }
+        for k in stiffness:
+            stiffness[k]*= .01
+            damping[k] *= .01
+        action_scale = 0.5
+        # decimation: Number of control action updates @ sim DT per policy DT
+        decimation = 10  # 100hz
+
+    class sim(LeggedRobotCfg.sim):
+        dt = 0.001  # 1000 Hz
+        substeps = 1  # 2
+        up_axis = 1  # 0 is y, 1 is z
+
+        class physx(LeggedRobotCfg.sim.physx):
+            num_threads = 12
+            solver_type = 0  # 0: pgs, 1: tgs
+            num_position_iterations = 4
+            num_velocity_iterations = 1
+            contact_offset = 0.01  # [m]
+            rest_offset = 0.0 #-0.02  # [m]
+            bounce_threshold_velocity = 0.1  # [m/s]
+            max_depenetration_velocity = 1.0
+            max_gpu_contact_pairs = 2**23  # 2**24 -> needed for 8000 envs and more
+            default_buffer_size_multiplier = 5
+            # 0: never, 1: last sub-step, 2: all sub-steps (default=2)
+            contact_collection = 2
+
+    class domain_rand:
+        randomize_friction = False
+        friction_range = [0.1, 2.0]
+
+        randomize_base_mass = False
+        added_mass_range = [-1.0, 1.0]
+        push_robots = False
+        push_interval_s = 4
+        max_push_vel_xy = 0.2
+        max_push_ang_vel = 0.4
+        dynamic_randomization = 0.02
+
+    class commands(LeggedRobotCfg.commands):
+        # Vers: lin_vel_x, lin_vel_y, ang_vel_yaw, heading (in heading mode ang_vel_yaw is recomputed from heading error)
+        num_commands = 4
+        resampling_time = 8.0  # time before command are changed[s]
+        heading_command = True  # if true: compute ang vel command from heading error
+
+        class ranges:
+            lin_vel_x = [-0.3, 0.6]  # min max [m/s]
+            lin_vel_y = [-0.3, 0.3]  # min max [m/s]
+            ang_vel_yaw = [-0.3, 0.3]  # min max [rad/s]
+            heading = [-3.14, 3.14]
+
+    class rewards:
+        # quite important to keep it right
+        base_height_target = 0.72
+        min_dist = 0.2
+        max_dist = 0.5
+        # put some settings here for LLM parameter tuning
+        target_joint_pos_scale = 0.17  # rad
+        target_feet_height = 0.06  # m
+        cycle_time = 0.64  # sec
+        # if true negative total rewards are clipped at zero (avoids early termination problems)
+        only_positive_rewards = True
+        # tracking reward = exp(error*sigma)
+        tracking_sigma = 5
+        max_contact_force = 400  # forces above this value are penalized
+
+        class scales:
+            # # reference motion tracking
+            # joint_pos = 1.6
+            # feet_clearance = 1.0
+            # feet_contact_number = 1.2
+            # # gait
+            # feet_air_time = 1.0
+            # foot_slip = -0.05
+            # feet_distance = 0.2
+            # knee_distance = 0.2
+            # # contact
+            # feet_contact_forces = -0.01
+            # # vel tracking
+            # tracking_lin_vel = 1.2
+            # tracking_ang_vel = 1.1
+            # vel_mismatch_exp = 0.5  # lin_z; ang x,y
+            # low_speed = 0.2
+            # track_vel_hard = 0.5
+
+            # above this was removed for standing policy
+            # base pos
+            default_joint_pos = 0.5
+            orientation = 1
+            base_height = 0.2
+
+            # energy
+            action_smoothness = -0.002
+            torques = -1e-5
+            dof_vel = -5e-4
+            dof_acc = -1e-7
+            base_acc = 0.2
+            collision = -1.0
+
+    class normalization:
+        class obs_scales:
+            #is2ac
+            lin_vel = 2.0
+            ang_vel = 1.0
+            dof_pos = 1.0
+            dof_vel = 0.05
+            quat = 1.0
+            height_measurements = 5.0
+
+        clip_observations = 18.0
+        clip_actions = 18.0
+
+    class viewer:
+        ref_env = 0
+        pos = [4, -4, 2]
+        lookat = [0, -2, 0]
+
+
+class OnlyLegsCfgPPO(LeggedRobotCfgPPO):
+    seed = 5
+    runner_class_name = "OnPolicyRunner"  # DWLOnPolicyRunner
+
+    class policy:
+        init_noise_std = 1.0
+        actor_hidden_dims = [512, 256, 128]
+        critic_hidden_dims = [768, 256, 128]
+
+    class algorithm(LeggedRobotCfgPPO.algorithm):
+        entropy_coef = 0.001
+        learning_rate = 1e-5
+        num_learning_epochs = 2
+        gamma = 0.994
+        lam = 0.9
+        num_mini_batches = 4
+
+    class runner:
+        policy_class_name = "ActorCritic"
+        algorithm_class_name = "PPO"
+        num_steps_per_env = 60  # per iteration
+        max_iterations = 10001  # number of policy updates
+
+        # logging
+        save_interval = 100  # check for potential saves every this many iterations
+        experiment_name = "Legs"
+        run_name = ""
+        # load and resume
+        resume = False
+        load_run = -1  # -1 = last run
+        checkpoint = -1  # -1 = last saved model
+        resume_path = None  # updated from load_run and chkpt