real-stanford/decentralized-multiarm

[CoRL 2020] Learning a Decentralized Multiarm Motion Planner

decentralized-multiarm's Issues

Gripper Open/Close

Hi,

It's a wonderful job!
I'm not very familiar with pybullet and not sure how to control the open and close of the gripper.
I read your https://github.com/columbia-robovision/decentralized-multiarm/tree/main/assets/gripper, but still not able to figure out.
Could you show how to open and close as in the animation of the last part of your paper?

How to continue training after being interrupted?

Hi @huy-ha ,

Thank you for reviewing this issue!

python main.py --config configs/default.json --tasks_path tasks/ --expert_waypoints expert/ --num_processes 8 --name multiarm_motion_planner_test

When I was training the network, the training was terminated due to a memory error

Traceback (most recent call last):
  File "main.py", line 83, in <module>
    simulate(args, config)
  File "main.py", line 73, in simulate
    remaining_observations=remaining_observations)
  File "main.py", line 41, in step_env
    for obs, env_id in ray.get(step_retval):
  File "/home/robot/anaconda3/envs/multiarm_local/lib/python3.7/site-packages/ray/worker.py", line 1452, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(RayOutOfMemoryError): ray::RRTSupervisionEnv.step() (pid=3255, ip=127.0.1.1)
  File "python/ray/_raylet.pyx", line 444, in ray._raylet.execute_task
  File "/home/robot/anaconda3/envs/multiarm_local/lib/python3.7/site-packages/ray/memory_monitor.py", line 140, in raise_if_low_memory
    self.error_threshold))
ray.memory_monitor.RayOutOfMemoryError: More than 95% of the memory on node robot-desktop is used (14.92 / 15.61 GB). The top 10 memory consumers are:

PID	MEM	COMMAND
3252	3.07GiB	ray::SACLearner.get_update()
3149	2.46GiB	python main.py --config configs/default.json --tasks_path tasks/ --expert_waypoints expert/ --num_pr
3259	0.92GiB	ray::RRTSupervisionEnv.step()
3256	0.92GiB	ray::RRTSupervisionEnv
3254	0.92GiB	ray::RRTSupervisionEnv
3253	0.9GiB	ray::RRTSupervisionEnv
3257	0.89GiB	ray::RRTSupervisionEnv
3258	0.7GiB	ray::TaskLoader
3260	0.7GiB	ray::RRTSupervisionEnv.step()
3250	0.7GiB	ray::RRTSupervisionEnv.step()

In addition, up to 0.07 GiB of shared memory is currently being used by the Ray object store. You can set the object store size with the `object_store_memory` parameter when starting Ray.
---
--- Tip: Use the `ray memory` command to list active objects in the cluster.
---

I want to start a new training task again based on the results when I stopped training.
For example:
The training mission was aborted for various reasons and the result is runs/multiarm_motion_planner/ckpt_multiarm_motion_planner_00101.
So I want to continue training for multiarm_motion_planner base on the result runs/multiarm_motion_planner/ckpt_multiarm_motion_planner_00101

I found '--curriculum_level', type=int, parameter in the code, but it does not seem to solve the problem.

Can you give me some suggestions?

ERROR worker.py:772 -- Exception raised in creation task: The actor died because of an error raised in its creation task, ray::TaskLoader.__init__()

Hi, @huy-ha

Thank you for your open source!

When I try to run python main.py --mode benchmark --tasks_path benchmark/ --load ours/ours.pth --num_processes 2 --gui, I get the follow errors. I try to fix them, but fail.

At first, the error is:

(TaskLoader pid=111640) 2023-04-12 22:54:24,948	ERROR worker.py:772 -- Exception raised in creation task: The actor died because of an error raised in its creation task, ray::TaskLoader.__init__() (pid=111640, ip=192.168.160.188)
(TaskLoader pid=111640)   File "/home/xhr/xhr/mulit_arm/decentralized-multiarm-main/environment/tasks.py", line 342, in __init__
(TaskLoader pid=111640)     assert len(self.files) > 0
(TaskLoader pid=111640) AssertionError

In the end, the error is:

Traceback (most recent call last):
  File "main.py", line 83, in <module>
    simulate(args, config)
  File "main.py", line 55, in simulate
    observations = ray.get([e.reset.remote() for e in envs])
  File "/home/xhr/anaconda3/envs/multiarm2/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/home/xhr/anaconda3/envs/multiarm2/lib/python3.7/site-packages/ray/_private/worker.py", line 2380, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError: ray::ParallelBenchmarkEnv.reset() (pid=111642, ip=192.168.160.188, repr=<environment.benchmarkEnv.ParallelBenchmarkEnv object at 0x7f48e1a42dd0>)
  File "/home/xhr/xhr/mulit_arm/decentralized-multiarm-main/environment/baseEnv.py", line 582, in reset
    self.setup_task()
  File "/home/xhr/xhr/mulit_arm/decentralized-multiarm-main/environment/baseEnv.py", line 617, in setup_task
    min_task_difficulty=self.min_task_difficulty)
  File "/home/xhr/xhr/mulit_arm/decentralized-multiarm-main/environment/tasks.py", line 282, in setup_next_task
    self.task_loader.get_next_task.remote())
ray.exceptions.RayActorError: The actor died because of an error raised in its creation task, ray::TaskLoader.__init__() (pid=111640, ip=192.168.160.188)
  File "/home/xhr/xhr/mulit_arm/decentralized-multiarm-main/environment/tasks.py", line 342, in __init__
    assert len(self.files) > 0
AssertionError

My conda environment is created from the provided environment.yml file. My system is ubuntu20.04.

Can you get me some suggestions? I need your help.

wget downloading problem

Hi @huy-ha, when I used wget to download raining tasks and expert demonstration dataset, it said, "Fail: reject the connection." Is there any other way to download raining tasks and expert demonstration dataset?

How many environments and tasks do you have?

How many environments and tasks do you have? It is like Gym which provides many environments?

Command to run bin placing task?

Hey, does this code currently include the bin placing task with the gripper?

I saw your comment here #1 (comment), but I wasn't sure.

If not, could that task and/or the command to run it be added?

I would like to know if “” --expert_trajectories“” provides example files. I didn't find in the readme. Thanks!

I didn't find an example of this mode running in the readme documentation
and What are the specific command parameters to run this function.

The exact function run location is here
decentralized-multiarm/policy/utils.py begin from line 149:
def setup_behaviour_clone(args, config, obs_dim, device):
dataset = BehaviourCloneDataset(args.expert_trajectories)

The parameters are defined as follows:
decentralized-multiarm/utils.py begin from line 100：
parser.add_argument('--expert_trajectories', type=str, default=None, help='path to expert trajectories directory' + 'for behaviour cloning')
Thanks!

Questions on Converting .p Files in robot0 Folder and Modifying Task Runners for Custom Tasks

Hello,

I've been working with the [demo] project and encountered a couple of issues that I need some guidance on. I successfully converted initial_configs.p and target_object_names.p in the benchmark folder from .p to .json, modified them, and converted them back to .p, which worked fine. However, I'm facing issues with the .p files in other folders, such as robot0.

Issue with Converting .p Files in robot0 Folder:
I tried a similar approach to convert and modify .p files in the robot0 folder but encountered errors during execution. I suspect that something might be getting lost or misinterpreted during the conversion process. Could you provide insights on what the .p files in these folders should contain or if there's a specific structure or content that I need to maintain while modifying these files?

Customizing Task Runners for Specific Tasks:
Additionally, I am interested in using this model to plan tasks where only the initial state and goal pose are provided, without the need for grasping and manipulating objects. Is it possible to modify the prepare_task_runners function to configure tasks based solely on initial states and goal poses? If yes, could you provide some guidance or examples on how to achieve this?

Any assistance or pointers on these matters would be greatly appreciated. Thank you for your support and for the great work on this project.

Under what conditions will the program end training?

Hi @huy-ha ,

Decentralized-multiarm is interesting work. Thank you for your work.

I read the code carefully and didn't find any indicator to stop training.

Will the program automatically stop training?

Or check whether the reward value reaches a certain value?

Evaluation error, tasks.py", line 342 assert len(self.files) > 0 AssertionError

Hi

I am getting the following error when I run the evaluation code, could you please help?

This is on Ubuntu 16.04, the following is the environment info: -

(multiarm) abdu@grasp-uoit:~/decentralized-multiarm$ python
Python 3.7.9 (default, Aug 31 2020, 12:42:55) 
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.__version__)
1.6.0
>>> torch.version.cuda
'10.2'
>>> torch.cuda.is_available()
True
>>> torch.cuda.current_device()
0
>>> torch.cuda.device(0)
<torch.cuda.device object at 0x7fa815a19c10>
>>> torch.cuda.device_count()
1
>>> torch.cuda.get_device_name(0)
'GeForce GTX 1080'
>>> 

(multiarm) abdu@grasp-uoit:~/decentralized-multiarm$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89

The output:-

(multiarm) abdu@grasp-uoit:~/decentralized-multiarm$ python main.py --mode benchmark --tasks_path benchmark/ --load ours/ours.pth --num_processes 1 --gui
pybullet build time: Jun 13 2021 11:25:20
[Setup] Parallelizing across 1 processes
2021-06-16 09:42:44,178	INFO services.py:1166 -- View the Ray dashboard at http://127.0.0.1:8265
[Setup] Creating task loader...
[Setup] Creating environments...
(pid=6025) pybullet build time: Jun 13 2021 11:25:20
(pid=6019) pybullet build time: Jun 13 2021 11:25:20
(pid=6025) [TaskLoader] WARNING: not repeating tasks.
(pid=6019) [BenchmarkEnv] config:
(pid=6019) 	episode_length:400
(pid=6019) 	action_interval:0.05
(pid=6019) 	simulation_time:20
(pid=6019) startThreads creating 1 threads.
(pid=6019) starting thread 0
(pid=6019) started thread 0 
(pid=6019) argc=2
(pid=6019) argv[0] = --unused
(pid=6019) argv[1] = --start_demo_name=Physics Server
(pid=6019) ExampleBrowserThreadFunc started
(pid=6019) X11 functions dynamically loaded using dlopen/dlsym OK!
(pid=6019) X11 functions dynamically loaded using dlopen/dlsym OK!
(pid=6019) Creating context
(pid=6019) Created GL 3.3 context
(pid=6019) Direct GLX rendering context obtained
(pid=6019) Making context current
(pid=6019) GL_VENDOR=NVIDIA Corporation
(pid=6019) GL_RENDERER=GeForce GTX 1080/PCIe/SSE2
(pid=6019) GL_VERSION=3.3.0 NVIDIA 440.33.01
(pid=6019) GL_SHADING_LANGUAGE_VERSION=3.30 NVIDIA via Cg compiler
(pid=6019) pthread_getconcurrency()=0
(pid=6019) Version = 3.3.0 NVIDIA 440.33.01
(pid=6019) Vendor = NVIDIA Corporation
(pid=6019) Renderer = GeForce GTX 1080/PCIe/SSE2
(pid=6019) b3Printf: Selected demo: Physics Server
(pid=6019) startThreads creating 1 threads.
(pid=6019) starting thread 0
(pid=6019) started thread 0 
(pid=6019) MotionThreadFunc thread started
(pid=6019) ven = NVIDIA Corporation
(pid=6019) ven = NVIDIA Corporation
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3P
2021-06-16 09:42:51,956	ERROR worker.py:1018 -- Possible unhandled error from worker: ray::TaskLoader.__init__() (pid=6025, ip=10.128.19.62)
  File "python/ray/_raylet.pyx", line 484, in ray._raylet.execute_task
  File "python/ray/_raylet.pyx", line 438, in ray._raylet.execute_task.function_executor
  File "/home/abdu/decentralized-multiarm/environment/tasks.py", line 342, in __init__
    assert len(self.files) > 0
AssertionError
(pid=6019) rintf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: ee_link
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: base
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: tool0
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp,126]:
(pid=6019) 
(pid=6019) b3Printf: No inertial data for link, using mass=1, localinertiadiagonal = 1,1,1, identity local inertial frame
(pid=6019) b3Printf: b3Warning[examples/Importers/ImportURDFD
(pid=6019) 	position_tolerance:0.02
(pid=6019) 	orientation_tolerance:0.1
(pid=6019) [BaseEnv] Setting up ray: Actor(ParallelBenchmarkEnv,7bbd902801000000)
Traceback (most recent call last):
  File "main.py", line 83, in <module>
    simulate(args, config)
  File "main.py", line 55, in simulate
    observations = ray.get([e.reset.remote() for e in envs])
  File "/home/abdu/anaconda3/envs/multiarm/lib/python3.7/site-packages/ray/worker.py", line 1428, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AssertionError): ray::ParallelBenchmarkEnv.reset() (pid=6019, ip=10.128.19.62)
  File "python/ray/_raylet.pyx", line 484, in ray._raylet.execute_task
  File "python/ray/_raylet.pyx", line 438, in ray._raylet.execute_task.function_executor
  File "/home/abdu/decentralized-multiarm/environment/baseEnv.py", line 582, in reset
    self.setup_task()
  File "/home/abdu/decentralized-multiarm/environment/baseEnv.py", line 617, in setup_task
    min_task_difficulty=self.min_task_difficulty)
  File "/home/abdu/decentralized-multiarm/environment/tasks.py", line 282, in setup_next_task
    self.task_loader.get_next_task.remote())
ray.exceptions.RayTaskError(AssertionError): ray::TaskLoader.get_next_task() (pid=6025, ip=10.128.19.62)
  File "python/ray/_raylet.pyx", line 445, in ray._raylet.execute_task
  File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task
  File "python/ray/_raylet.pyx", line 483, in ray._raylet.execute_task
  File "python/ray/_raylet.pyx", line 484, in ray._raylet.execute_task
  File "python/ray/_raylet.pyx", line 438, in ray._raylet.execute_task.function_executor
  File "/home/abdu/decentralized-multiarm/environment/tasks.py", line 342, in __init__
    assert len(self.files) > 0
AssertionError
(pid=6021) [SAC] loading networks from  ours/ours.pth
(pid=6021) [SAC] Continuing from time step  667534701  and update step  750000
(pid=6021) StochasticActor(
(pid=6021)   (seq_net): LSTM(107, 256, batch_first=True)
(pid=6021)   (net): Sequential(
(pid=6021)     (0): Linear(in_features=256, out_features=128, bias=True)
(pid=6021)     (1): Tanh()
(pid=6021)     (2): Linear(in_features=128, out_features=64, bias=True)
(pid=6021)     (3): Tanh()
(pid=6021)     (4): Linear(in_features=64, out_features=12, bias=True)
(pid=6021)     (5): Tanh()
(pid=6021)   )
(pid=6021) )
(pid=6021) Q(
(pid=6021)   (seq_net): LSTM(107, 256, batch_first=True)
(pid=6021)   (net): Sequential(
(pid=6021)     (0): Linear(in_features=262, out_features=128, bias=True)
(pid=6021)     (1): Tanh()
(pid=6021)     (2): Linear(in_features=128, out_features=64, bias=True)
(pid=6021)     (3): Tanh()
(pid=6021)     (4): Linear(in_features=64, out_features=1, bias=True)
(pid=6021)   )
(pid=6021) )

or

(multiarm) abdu@grasp-uoit:~/decentralized-multiarm$ python benchmark_dynamic.py --mode benchmark --tasks_path benchmark/ --load ours/ours.pth --num_processes 1 --gui
pybullet build time: Jun 13 2021 11:25:20
2021-06-16 09:42:12,381	INFO services.py:1166 -- View the Ray dashboard at http://127.0.0.1:8265
[TaskLoader] WARNING: not repeating tasks.
Traceback (most recent call last):
  File "benchmark_dynamic.py", line 48, in <module>
    repeat=False)
  File "/home/abdu/decentralized-multiarm/environment/tasks.py", line 342, in __init__
    assert len(self.files) > 0
AssertionError

Training too slow problem

Hello @huy-ha , thank you for your open-source and giving such clear guidance.

A promblem

When I run the following command：
python main.py --mode benchmark --tasks_path benchmark/ --load ours/ours.pth --num_processes 1 --gui

The question is that:

Fatal Python error: Aborted
Stack (most recent call first):
  File "F:\anaconda\envs\multiarm\lib\site-packages\ray\worker.py", line 1244 in connect
  File "F:\anaconda\envs\multiarm\lib\site-packages\ray\worker.py", line 768 in init
  File "F:\anaconda\envs\multiarm\lib\site-packages\ray\_private\client_mode_hook.py", line 47 in wrapper
  File "F:\Graduate\decentralized-multiarm-main\utils.py", line 378 in initialize_ray
  File "F:\Graduate\decentralized-multiarm-main\utils.py", line 448 in setup
  File "main.py", line 50 in simulate
  File "main.py", line 83 in <module>

Direct error, resulting in the program can not run。

AttributeError: 'NoneType' object has no attribute 'rsample'

Hi, @huy-ha

Thank you for your open source!

When I try to run python main.py --config configs/default.json --tasks_path tasks/ --expert_waypoints expert/ --num_processes 1 --name multiarm_motion_planner, I get the follow error. I try to fix it, but fail.

Traceback (most recent call last):
  File "main.py", line 83, in <module>
    simulate(args, config)
  File "main.py", line 68, in simulate
    for observation in observations]
  File "main.py", line 68, in <listcomp>
    for observation in observations]
  File "C:\1 learning a decentralized multiarm motion planner\decentralized-multiarm\policy\SAC.py", line 165, in act
    reparametrize=self.reparametrize)
  File "C:\anaconda3\envs\multiarm\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\1 learning a decentralized multiarm motion planner\decentralized-multiarm\policy\BaseNet.py", line 187, in forward
    actions = dist.rsample()
AttributeError: 'NoneType' object has no attribute 'rsample'

My python version is pytorch 1.7.0 py3.7_cuda110_cudnn8_0.

I have to use pytorch 1.7.0 because cuda 11 no pytorch 1.6.0 .

Can you get me some suggestion?

About pretrained weights

Hi @huy-ha ,
Decentralized-multiarm is an impressive work
thank you for your sharing.
I want to know the pretrained weights are trained by RL or just behavior clone?
In addition, is the checkpoint.pth in demo/ the final weight?

Add a license like apache 2?

This looks interesting! Is there any chance you can add a license like a so we can download run and modify the code without legal concerns?

One suggested choice is Apache 2, which is the one used by tensorflow and is compatible with pytorch. Here is a summary:
https://tldrlegal.com/license/apache-license-2.0-(apache-2.0)

Here is the text:

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