How Install IssacLab on unbuntu 20.04
Follow the official documentation
Installation using Isaac Sim Binaries
Train a robot error
Error executing job with overrides: []
Traceback (most recent call last):
File "/home/gao/IsaacLab/source/isaaclab_tasks/isaaclab_tasks/utils/hydra.py", line 101, in hydra_main
func(env_cfg, agent_cfg, *args, **kwargs)
File "/home/gao/IsaacLab/scripts/reinforcement_learning/rsl_rl/train.py", line 129, in main
runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
File "/home/gao/miniforge3/envs/env_isaaclab/lib/python3.10/site-packages/rsl_rl/runners/on_policy_runner.py", line 44, in __init__
if self.alg_cfg["rnd_cfg"] is not None:
KeyError: 'rnd_cfg'
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
2025-02-12 08:51:32 [20,276ms] [Warning] [omni.fabric.plugin] gFabricState->gUsdStageToSimStageWithHistoryMap had 1 outstanding SimStageWithHistory(s) at shutdown
2025-02-12 08:51:32 [20,847ms] [Warning] [carb] Recursive unloadAllPlugins() detected!
how to solve
File 1 :/home/gao/miniforge3/envs/env_isaaclab/lib/python3.10/site-packages/rsl_rl/runners/on_policy_runner.py
(env_isaaclab) gao@rs-controlserver1:~/IsaacLab$ python -c "import rsl_rl.runners.on_policy_runner; print(rsl_rl.runners.on_policy_runner.__file__)"
/home/gao/miniforge3/envs/env_isaaclab/lib/python3.10/site-packages/rsl_rl/runners/on_policy_runner.py
Open the File for Editing:
For example, using code:
code /home/gao/miniforge3/envs/env_isaaclab/lib/python3.10/site-packages/rsl_rl/runners/on_policy_runner.py
Locate the Problematic Code:
Search (using Ctrl+F in code) for rnd_cfg. Look for a line similar to:
if self.alg_cfg["rnd_cfg"] is not None:
Modify the Code:
Change that line to:
if self.alg_cfg.get("rnd_cfg") is not None:
This uses the dictionary’s get method, which returns None (or a default value if you specify one) if the key is missing, thus avoiding the KeyError.
Save and Exit:
File 2 ./source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/anymal_c/agents/rsl_rl_ppo_cfg.py
. Update the Configuration Schema
A more permanent solution is to ensure that the configuration schema includes “symmetry_cfg”. For example, if your configuration is defined in a dataclass (likely in a file like rsl_rl_ppo_cfg.py), add a default field for “symmetry_cfg”. For example:
from dataclasses import dataclass, field
from typing import Optional, Dict
@dataclass
class AnymalCRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
# ... other fields ...
symmetry_cfg: Optional[Dict] = field(default_factory=dict)
Make sure you modify the version of the file that is actually being used (in your case, the one in the manager-based configuration if that’s what your logs indicate).
rerun the training command
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 --headless
The output looks like this:
Actor MLP: Sequential(
(0): Linear(in_features=235, out_features=512, bias=True)
(1): ELU(alpha=1.0)
(2): Linear(in_features=512, out_features=256, bias=True)
(3): ELU(alpha=1.0)
(4): Linear(in_features=256, out_features=128, bias=True)
(5): ELU(alpha=1.0)
(6): Linear(in_features=128, out_features=12, bias=True)
)
Critic MLP: Sequential(
(0): Linear(in_features=235, out_features=512, bias=True)
(1): ELU(alpha=1.0)
(2): Linear(in_features=512, out_features=256, bias=True)
(3): ELU(alpha=1.0)
(4): Linear(in_features=256, out_features=128, bias=True)
(5): ELU(alpha=1.0)
(6): Linear(in_features=128, out_features=1, bias=True)
)
/home/gao/isaac-sim/isaac-sim-standalone@4.5.0/exts/omni.isaac.ml_archive/pip_prebundle/torch/nn/modules/module.py:1747: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:1410.)
return forward_call(*args, **kwargs)
################################################################################
Learning iteration 0/1500
Computation: 17687 steps/s (collection: 5.312s, learning 0.246s)
Value function loss: 0.0365
Surrogate loss: 0.0010
Mean action noise std: 1.00
Mean total reward: -0.35
Mean episode length: 11.87
Episode_Reward/track_lin_vel_xy_exp: 0.0029
Episode_Reward/track_ang_vel_z_exp: 0.0020
Episode_Reward/lin_vel_z_l2: -0.0098
Episode_Reward/ang_vel_xy_l2: -0.0031
Episode_Reward/dof_torques_l2: -0.0013
Episode_Reward/dof_acc_l2: -0.0049
Episode_Reward/action_rate_l2: -0.0028
Episode_Reward/feet_air_time: -0.0003
Episode_Reward/undesired_contacts: -0.0009
Episode_Reward/flat_orientation_l2: 0.0000
Episode_Reward/dof_pos_limits: 0.0000
Curriculum/terrain_levels: 3.5276
Metrics/base_velocity/error_vel_xy: 0.0173
Metrics/base_velocity/error_vel_yaw: 0.0172
Episode_Termination/time_out: 3.5833
Episode_Termination/base_contact: 0.0000
--------------------------------------------------------------------------------
Total timesteps: 98304
Iteration time: 5.56s
Total time: 5.56s
ETA: 8336.5s
Could not find git repository in /home/gao/miniforge3/envs/env_isaaclab/lib/python3.10/site-packages/rsl_rl/__init__.py. Skipping.
Storing git diff for 'IsaacLab' in: /home/gao/IsaacLab/logs/rsl_rl/anymal_c_rough/2025-02-12_10-49-33/git/IsaacLab.diff
/home/gao/isaac-sim/isaac-sim-standalone@4.5.0/exts/omni.isaac.ml_archive/pip_prebundle/torch/nn/modules/module.py:1747: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:1410.)
return forward_call(*args, **kwargs)
################################################################################
Learning iteration 1/1500
Computation: 25482 steps/s (collection: 3.705s, learning 0.152s)
Value function loss: 0.0194
Surrogate loss: -0.0062
Mean action noise std: 0.99
Mean total reward: -1.09
Mean episode length: 39.25
Episode_Reward/track_lin_vel_xy_exp: 0.0075
Episode_Reward/track_ang_vel_z_exp: 0.0052
Episode_Reward/lin_vel_z_l2: -0.0199
Episode_Reward/ang_vel_xy_l2: -0.0094
Episode_Reward/dof_torques_l2: -0.0043
Episode_Reward/dof_acc_l2: -0.0143
Episode_Reward/action_rate_l2: -0.0086
Episode_Reward/feet_air_time: -0.0010
Episode_Reward/undesired_contacts: -0.0051
Episode_Reward/flat_orientation_l2: 0.0000
Episode_Reward/dof_pos_limits: 0.0000
Curriculum/terrain_levels: 3.5047
Metrics/base_velocity/error_vel_xy: 0.0606
Metrics/base_velocity/error_vel_yaw: 0.0597
Episode_Termination/time_out: 4.1250
Episode_Termination/base_contact: 0.8750
--------------------------------------------------------------------------------
Total timesteps: 196608
Iteration time: 3.86s
Total time: 9.42s
ETA: 7056.9s
################################################################################
Learning iteration 2/1500
Computation: 23548 steps/s (collection: 4.017s, learning 0.158s)
Value function loss: 0.0166
Surrogate loss: -0.0090
Mean action noise std: 0.97
Mean total reward: -1.50
Mean episode length: 63.07
Episode_Reward/track_lin_vel_xy_exp: 0.0118
Episode_Reward/track_ang_vel_z_exp: 0.0095
Episode_Reward/lin_vel_z_l2: -0.0200
Episode_Reward/ang_vel_xy_l2: -0.0145
Episode_Reward/dof_torques_l2: -0.0075
Episode_Reward/dof_acc_l2: -0.0231
Episode_Reward/action_rate_l2: -0.0144
Episode_Reward/feet_air_time: -0.0019
Episode_Reward/undesired_contacts: -0.0138
Episode_Reward/flat_orientation_l2: 0.0000
Episode_Reward/dof_pos_limits: 0.0000
Curriculum/terrain_levels: 3.4744
Metrics/base_velocity/error_vel_xy: 0.0981
Metrics/base_velocity/error_vel_yaw: 0.0947
Episode_Termination/time_out: 4.1250
Episode_Termination/base_contact: 1.2500
--------------------------------------------------------------------------------
Total timesteps: 294912
Iteration time: 4.17s
Total time: 13.59s
ETA: 6785.9s