[bugfix] v_template

LICENSE

@@ -0,0 +1,28 @@
+BSD 3-Clause License
+
+Copyright (c) 2024, Zhengyi Luo
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

MANIFEST.in

@@ -0,0 +1,5 @@
+include LICENSE
+include README.md
+recursive-include smpl_sim *.py
+recursive-include smpl_sim/utils *.py
+recursive-include smpl_sim/data *.yaml *.urdf *.xml

MUJOCO_LOG.TXT

@@ -1,3 +1,9 @@
 Sun Jan 28 12:48:00 2024
 ERROR: could not initialize GLFW
 
+Mon Feb 19 20:01:16 2024
+WARNING: mju_openResource: unknown file 'tmp/smpl_humanoid.xml'
+
+Sat Mar  2 18:16:01 2024
+ERROR: could not initialize GLFW
+

README.md

@@ -1,29 +1,58 @@
 [Repo still under construction]
-# Porting of UHC and PHC to MUJOCO>=3
+# SMPLSim: Simulating SMPL/SMPLX Humanoids in MUJOCO and Isaac Gym
 
 `smpl_sim` is a pip-installable library containing a modelization of the SMPL humanoid in different simulators (MUJOCO and Isaac Gym). It is a minimal library to support simple humanoid tasks, and is the basis library for doing more complicated tasks such as motion imitation. 
 
+<div float="center">
+  <img src="assets/smplsim_teaser.gif" />
+</div>
+
+
+
+## Core Features
+This repo supports creating the xml files for SMPL/SMPLH/SMPLX compatible humanoid models in MUJOCO>=3. It also supports creating the humanoid models in Isaac Gym. It also provides a simple PPO implementation for training the humanoid models in MUJOCO. 
+
 ### Commands:
 
 ```
 python examples/env_humanoid_test.py headless=False
-python smpl_sim/run.py env=speed exp_name=speed env.self_obs_v=2 
-python smpl_sim/run.py env=getup exp_name=speed env.self_obs_v=2 
-python smpl_sim/run.py env=reach exp_name=speed env.self_obs_v=2 
+python smpl_sim/run.py env=speed exp_name=speed env.self_obs_v=2  robot.create_vel_sensors=True
+python smpl_sim/run.py env=getup exp_name=speed env.self_obs_v=2 robot.create_vel_sensors=True
+python smpl_sim/run.py env=reach exp_name=speed env.self_obs_v=2 robot.create_vel_sensors=True
 ```
 
 
 ## Citation
 If you find this work useful for your research, please cite our paper:
 ```
+PULSE:
+@inproceedings{
+luo2024universal,
+title={Universal Humanoid Motion Representations for Physics-Based Control},
+author={Zhengyi Luo and Jinkun Cao and Josh Merel and Alexander Winkler and Jing Huang and Kris M. Kitani and Weipeng Xu},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=OrOd8PxOO2}
+}
+
+PHC:
 @inproceedings{Luo2023PerpetualHC,
     author={Zhengyi Luo and Jinkun Cao and Alexander W. Winkler and Kris Kitani and Weipeng Xu},
     title={Perpetual Humanoid Control for Real-time Simulated Avatars},
     booktitle={International Conference on Computer Vision (ICCV)},
     year={2023}
-}            
+}           
+
+UHC:
+@inproceedings{Luo2021DynamicsRegulatedKP,
+  title={Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation},
+  author={Zhengyi Luo and Ryo Hachiuma and Ye Yuan and Kris Kitani},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2021}
+}
 ```
 
+
 Also consider citing these prior works that are used in this project:
 
 ```
@@ -42,9 +71,4 @@ Also consider citing these prior works that are used in this project:
     year={2023}
 }     
 
-@inproceedings{Luo2021DynamicsRegulatedKP,
-  title={Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation},
-  author={Zhengyi Luo and Ryo Hachiuma and Ye Yuan and Kris Kitani},
-  booktitle={Advances in Neural Information Processing Systems},
-  year={2021}
-}
+```

examples/motion_lib_test.py

@@ -35,9 +35,8 @@
 def main(cfg : DictConfig) -> None:
     # motions = joblib.load("sample_data/amass_isaac_standing_upright_slim.pkl")
     device = torch.device("cpu")
-    # motion_file = "/hdd/zen/data/ActBound/AMASS/amass_copycat_take6_test.pkl"
-    # motion_file = "data/amass/singles/0-KIT_3_walking_slow08_poses.pkl"
-    motion_file = "data/amass/singles/0-SSM_synced_20160930_50032_punch_kick_sync_poses.pkl"
+    motion_file = "data/amass/singles/0-KIT_3_walking_slow08_poses.pkl"
+    # motion_file = "data/amass/singles/0-SSM_synced_20160930_50032_punch_kick_sync_poses.pkl"
     motion_lib_cfg = EasyDict({
         "motion_file": motion_file,
         "device": device,
@@ -50,7 +49,8 @@ def main(cfg : DictConfig) -> None:
         "randomrize_heading": True,
     })
     motion_lib = MotionLibSMPL(motion_lib_cfg)
-    motion_lib.load_motions(shape_params = [np.zeros(17)] )
+    shape_params = np.zeros(17)
+    motion_lib.load_motions(m_cfg = motion_lib_cfg, shape_params = [shape_params] )
 
     env = HumanoidEnv(cfg)
     env.reset()

pyproject.toml

@@ -34,6 +34,7 @@ dependencies = [
   'gymnasium>=0.29.1',
   'hydra-core>=1.3',
   'tqdm',
+  'easydict',
   "importlib-resources==3.0.0;python_version<'3.9'"
 ]
 

smpl_sim/agents/agent_humanoid.py

@@ -180,22 +180,23 @@ def log_train(self, info):
 
 
         if not self.cfg.no_log:
-            wandb.log(
-                data={
+            log_data = {
                     "avg_episode_reward": loggers.avg_episode_reward,
                     "eps_len": loggers.avg_episode_len,
                     "avg_rwd": loggers.avg_reward,
                     "reward_raw": loggers.info_dict,
-                },
-                step=self.epoch,
-            )
-
+                }
+
             if "log_eval" in info:
-                wandb.log(data=info["log_eval"], step=self.epoch)
+                log_data.update(info["log_eval"])
+
+            wandb.log(data=log_data, step=self.epoch)
+
 
     def optimize_policy(self,save_model=True):
         starting_epoch = self.epoch
         for _ in range(starting_epoch, self.cfg.learning.max_epoch):
+            info = {}
             t0 = time.time()
             self.pre_epoch()
             batch, loggers = self.sample(self.cfg.learning.min_batch_size)
@@ -213,13 +214,12 @@ def optimize_policy(self,save_model=True):
             elif save_model and (self.epoch) % self.cfg.learning.save_curr_frequency == 0:
                 self.save_curr()
             t2 = time.time()
-
-            info = {
+            info.update({
                 "loggers": loggers,
                 "T_sample": t1 - t0,
                 "T_update": t2 - t1,
                 "T_total": t2 - t0,
-            }
+            })
 
             self.log_train(info)
             self.after_epoch()

smpl_sim/envs/humanoid_env.py

@@ -12,6 +12,7 @@
 from scipy.spatial.transform import Rotation as sRot
 from enum import Enum
 from collections import defaultdict
+import torch
 
 from smpl_sim.envs.base_env import BaseEnv
 import smpl_sim.envs.controllers as ctrls

smpl_sim/smpllib/motion_lib_base.py

@@ -262,7 +262,7 @@ def update_soft_sampling_weight(self, failed_keys):
 
     def update_sampling_prob(self, termination_history):
         print("------------------------------------------------------ Restoring Termination History ------------------------------------------------------")
-        if len(termination_history) == len(self._termination_history):
+        if len(self._sampling_prob) == len(self._termination_history):
             self._sampling_prob[:] = termination_history/termination_history.sum()
             self._termination_history = termination_history
             print("Successfully restored termination history")

smpl_sim/smpllib/motion_lib_smpl.py

@@ -113,7 +113,7 @@ def load_motion_with_skeleton(ids, motion_data_list, shape_params, mesh_parsers,
                 start = random.randint(0, seq_len - max_len)
                 end = start + max_len
 
-            trans = to_torch(curr_file['trans']).float().clone()[start:end]
+            trans = to_torch(curr_file['trans'] if "trans" in curr_file else curr_file['trans_orig']).float().clone()[start:end]
 
             pose_aa = to_torch(curr_file['pose_aa'][start:end]).float().clone()
             if pose_aa.shape[1] == 156:

smpl_sim/smpllib/np_smpl_humanoid_batch.py

@@ -5,20 +5,20 @@
 import sys
 import pdb
 import os.path as osp
-from copycat.utils.torch_ext import dict_to_torch
+from smpl_sim.utils.torch_ext import dict_to_torch
 
 sys.path.append(os.getcwd())
 
-from copycat.utils.torch_utils import *
-from copycat.utils.transform_utils import *
+from smpl_sim.utils.torch_utils import *
+from smpl_sim.utils.np_transform_utils import *
 from scipy.spatial.transform import Rotation as sRot
 import joblib
 from mujoco_py import load_model_from_path
-from copycat.smpllib.smpl_mujoco import SMPLConverter, smpl_to_qpose, smpl_to_qpose_torch, SMPL_BONE_ORDER_NAMES
-from copycat.smpllib.smpl_parser import SMPL_EE_NAMES
-from copycat.utils.tools import get_expert, get_expert_master
+from smpl_sim.smpllib.smpl_joint_names import SMPLConverter, smpl_to_qpose, smpl_to_qpose_torch, SMPL_BONE_ORDER_NAMES
+from smpl_sim.smpllib.smpl_parser import SMPL_EE_NAMES
+from smpl_sim.utils.tools import get_expert, get_expert_master
 import pytorch3d.transforms as tR
-from copycat.smpllib.smpl_parser import (
+from smpl_sim.smpllib.smpl_parser import (
     SMPL_Parser,
     SMPLH_Parser,
     SMPLX_Parser,
@@ -406,12 +406,12 @@ def forward_kinematics_batch(self, rotations, root_rotations,
 
 if __name__ == "__main__":
     import mujoco_py
-    from copycat.smpllib.smpl_robot import Robot
-    from copycat.smpllib.torch_smpl_humanoid import Humanoid
-    from copycat.utils.config_utils.copycat_config import Config
-    from copycat.data_loaders.dataset_amass_single import DatasetAMASSSingle
-    from copycat.utils.torch_ext import dict_to_torch
-    from copycat.smpllib.smpl_mujoco import smpl_to_qpose_torch, smplh_to_smpl
+    from smpl_sim.smpllib.smpl_robot import Robot
+    from smpl_sim.smpllib.torch_smpl_humanoid import Humanoid
+    from smpl_sim.utils.config_utils.copycat_config import Config
+    from smpl_sim.data_loaders.dataset_amass_single import DatasetAMASSSingle
+    from smpl_sim.utils.torch_ext import dict_to_torch
+    from smpl_sim.smpllib.smpl_mujoco import smpl_to_qpose_torch, smplh_to_smpl
     torch.manual_seed(0)
 
     cfg = Config(