| Name | rex-lib JSON |
| Version |
0.0.9
JSON |
| download |
| home_page | None |
| Summary | Robotic Environments with jaX (REX) |
| upload_time | 2024-12-14 21:44:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
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| keywords |
jax
reinforcement-learning
robotics
sim2real
simulation
|
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<h1 align="center">Rex: Robotic Environments with jaX</h1>
<p align="center">
<a href="https://opensource.org/licenses/Apache-2.0">
<img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="license">
</a>
<a href="https://pep8.org/">
<img src="https://img.shields.io/badge/code%20style-pep8-000000.svg" alt="PEP8">
</a>
<a href="https://github.com/psf/black">
<img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="codestyle">
</a>
</p>
Rex is a [JAX](https://github.com/google/jax)-powered framework for sim-to-real robotics.
Key features:
- **Graph-based design**: Model asynchronous systems with nodes for sensing, actuation, and computation.
- **Latency-aware modeling**: Simulate delay effects for hardware, computation, and communication channels.
- **Real-time and parallelized runtimes**: Run real-world experiments or accelerated parallelized simulations.
- **Seamless integration with JAX**: Utilize JAX's autodiff, JIT compilation, and GPU/TPU acceleration.
- **System identification tools**: Estimate dynamics and delays directly from real-world data.
- **Modular and extensible**: Compatible with various simulation engines (e.g., [Brax](https://github.com/google/brax), [MuJoCo](https://mujoco.readthedocs.io/en/stable/mjx.html)).
- **Unified sim2real pipeline**: Train delay-aware policies in simulation and deploy them on real-world systems.
## Sim-to-Real Workflow
1. **Interface Real Systems**: Define nodes for sensors, actuators, and computation to represent real-world systems.
2. **Build Simulation**: Swap real-world nodes with simulated ones (e.g., physics engines, motor dynamics).
3. **System Identification**: Estimate system dynamics and delays from real-world data.
4. **Policy Training**: Train delay-aware policies in simulation, accounting for realistic dynamics and delays.
5. **Evaluation**: Evaluate trained policies on the real-world system, and iterate on the design.
## Installation
```bash
pip install rex-lib
```
Requires Python 3.9+ and JAX 0.4.30+.
## Documentation
Available at [rex-lib.github.io](https://rex-lib.github.io/).
## Quick example
Here's a simple example of a pendulum system.
The real-world system is defined with nodes interfacing hardware for sensing, actuation:
```python
from rex.asynchronous import AsyncGraph
from rex.examples.pendulum import Actuator, Agent, Sensor
sensor = Sensor(rate=50) # 50 Hz sampling rate
agent = Agent(rate=30) # 30 Hz policy execution rate
actuator = Actuator(rate=50) # 50 Hz control rate
nodes = dict(sensor=sensor, agent=agent, actuator=actuator)
agent.connect(sensor) # Agent receives sensor data
actuator.connect(agent) # Actuator receives agent commands
graph = AsyncGraph(nodes, agent) # Graph for real-world execution
graph_state = graph.init() # Initial states of all nodes
graph.warmup(graph_state) # Jit-compiles the graph (only once).
for _ in range(100): # Run the graph for 100 steps
graph_state = graph.run(graph_state) # Run for one step
graph.stop() # Stop asynchronous nodes
data = graph.get_record() # Get recorded data from the graph
```
In simulation, we replace the hardware-interfacing nodes with simulated ones, add delay models, and add a physics simulation node:
```python
from distrax import Normal
from rex.constants import Clock, RealTimeFactor
from rex.asynchronous import AsyncGraph
from rex.examples.pendulum import SimActuator, Agent, SimSensor, BraxWorld
sensor = SimSensor(rate=50, delay_dist=Normal(0.01, 0.001)) # Process delay
agent = Agent(rate=30, delay_dist=Normal(0.02, 0.005)) # Computational delay
actuator = SimActuator(rate=50, delay_dist=Normal(0.01, 0.001)) # Process delay
world = BraxWorld(rate=100) # 100 Hz physics simulation
nodes = dict(sensor=sensor, agent=agent, actuator=actuator, world=world)
sensor.connect(world, delay_dist=Normal(0.001, 0.001)) # Sensor delay
agent.connect(sensor, delay_dist=Normal(0.001, 0.001)) # Communication delay
actuator.connect(agent, delay_dist=Normal(0.001, 0.001)) # Communication delay
world.connect(actuator, delay_dist=Normal(0.001, 0.001), # Actuator delay
skip=True) # Breaks algebraic loop in the graph
graph = AsyncGraph(nodes, agent,
clock=Clock.SIMULATED, # Simulates based on delay_dist
real_time_factor=RealTimeFactor.FAST_AS_POSSIBLE)
graph_state = graph.init() # Initial states of all nodes
graph.warmup(graph_state) # Jit-compiles the graph
for _ in range(100): # Run the graph for 100 steps
graph_state = graph.run(graph_state) # Run for one step
graph.stop() # Stop asynchronous nodes
data = graph.get_record() # Get recorded data from the graph
```
Nodes are defined using JAX's [PyTrees](https://jax.readthedocs.io/en/latest/pytrees.html):
```python
from rex.node import BaseNode
class Agent(BaseNode):
def init_params(self, rng=None, graph_state=None):
return SomePyTree(a=..., b=...)
def init_state(self, rng=None, graph_state=None):
return SomePyTree(x1=..., x2=...)
def init_output(self, rng=None, graph_state=None):
return SomePyTree(y1=..., y2=...)
# Jit-compiled via graph.warmup for faster execution
def step(self, step_state): # Called at Node's rate
ss = step_state # Shorten name
# Read params, and current state
params, state = ss.params, ss.state
# Current episode, sequence, timestamp
eps, seq, ts = ss.eps, ss.seq, ss.ts
# Grab the data, and I/O timestamps
cam = ss.inputs["sensor"] # Received messages
cam.data, cam.ts_send, cam.ts_recv
... # Some computation for new_state, output
new_state = SomePyTree(x1=..., x2=...)
output = SomePyTree(y1=..., y2=...)
# Update step_state for next step call
new_ss = ss.replace(state=new_state)
return new_ss, output # Sends output
```
## Next steps
If this quick start has got you interested, then have a look at the [sim2real.ipynb](https://github.com/anonymous/rex/blob/master/examples/sim2real.ipynb) notebook for an example of a sim-to-real workflow using Rex.
## Citation
If you are using rex for your scientific publications, please cite:
```bibtex
@article{anonymous2024rex,
title={{REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation}},
author={Anonymized},
journal={OpenReview},
year={2024}
}
```
Raw data
{
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"home_page": null,
"name": "rex-lib",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "jax, reinforcement-learning, robotics, sim2real, simulation",
"author": null,
"author_email": "Anonymous <anonymous@hotmail.com>",
"download_url": "https://files.pythonhosted.org/packages/9a/9f/e5dd7f38d77b4548a031dac508efb5fe111f36398c4dca3eccf3bc34e6e1/rex_lib-0.0.9.tar.gz",
"platform": null,
"description": "<h1 align=\"center\">Rex: Robotic Environments with jaX</h1>\n\n<p align=\"center\">\n <a href=\"https://opensource.org/licenses/Apache-2.0\">\n <img src=\"https://img.shields.io/badge/License-Apache_2.0-blue.svg\" alt=\"license\">\n </a>\n <a href=\"https://pep8.org/\">\n <img src=\"https://img.shields.io/badge/code%20style-pep8-000000.svg\" alt=\"PEP8\">\n </a>\n <a href=\"https://github.com/psf/black\">\n <img src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" alt=\"codestyle\">\n </a>\n</p>\n\nRex is a [JAX](https://github.com/google/jax)-powered framework for sim-to-real robotics.\n\nKey features:\n\n- **Graph-based design**: Model asynchronous systems with nodes for sensing, actuation, and computation.\n- **Latency-aware modeling**: Simulate delay effects for hardware, computation, and communication channels.\n- **Real-time and parallelized runtimes**: Run real-world experiments or accelerated parallelized simulations.\n- **Seamless integration with JAX**: Utilize JAX's autodiff, JIT compilation, and GPU/TPU acceleration.\n- **System identification tools**: Estimate dynamics and delays directly from real-world data.\n- **Modular and extensible**: Compatible with various simulation engines (e.g., [Brax](https://github.com/google/brax), [MuJoCo](https://mujoco.readthedocs.io/en/stable/mjx.html)).\n- **Unified sim2real pipeline**: Train delay-aware policies in simulation and deploy them on real-world systems.\n\n## Sim-to-Real Workflow\n\n1. **Interface Real Systems**: Define nodes for sensors, actuators, and computation to represent real-world systems.\n2. **Build Simulation**: Swap real-world nodes with simulated ones (e.g., physics engines, motor dynamics).\n3. **System Identification**: Estimate system dynamics and delays from real-world data.\n4. **Policy Training**: Train delay-aware policies in simulation, accounting for realistic dynamics and delays.\n5. **Evaluation**: Evaluate trained policies on the real-world system, and iterate on the design.\n\n## Installation\n\n```bash\npip install rex-lib\n```\n\nRequires Python 3.9+ and JAX 0.4.30+.\n\n## Documentation\nAvailable at [rex-lib.github.io](https://rex-lib.github.io/).\n\n## Quick example\n\nHere's a simple example of a pendulum system. \nThe real-world system is defined with nodes interfacing hardware for sensing, actuation:\n```python\nfrom rex.asynchronous import AsyncGraph\nfrom rex.examples.pendulum import Actuator, Agent, Sensor\n\nsensor = Sensor(rate=50) # 50 Hz sampling rate\nagent = Agent(rate=30) # 30 Hz policy execution rate\nactuator = Actuator(rate=50) # 50 Hz control rate\nnodes = dict(sensor=sensor, agent=agent, actuator=actuator)\n\nagent.connect(sensor) # Agent receives sensor data\nactuator.connect(agent) # Actuator receives agent commands\ngraph = AsyncGraph(nodes, agent) # Graph for real-world execution\n\ngraph_state = graph.init() # Initial states of all nodes\ngraph.warmup(graph_state) # Jit-compiles the graph (only once).\nfor _ in range(100): # Run the graph for 100 steps\n graph_state = graph.run(graph_state) # Run for one step\ngraph.stop() # Stop asynchronous nodes\ndata = graph.get_record() # Get recorded data from the graph\n```\nIn simulation, we replace the hardware-interfacing nodes with simulated ones, add delay models, and add a physics simulation node:\n```python\nfrom distrax import Normal\nfrom rex.constants import Clock, RealTimeFactor\nfrom rex.asynchronous import AsyncGraph\nfrom rex.examples.pendulum import SimActuator, Agent, SimSensor, BraxWorld\n\nsensor = SimSensor(rate=50, delay_dist=Normal(0.01, 0.001)) # Process delay\nagent = Agent(rate=30, delay_dist=Normal(0.02, 0.005)) # Computational delay\nactuator = SimActuator(rate=50, delay_dist=Normal(0.01, 0.001)) # Process delay\nworld = BraxWorld(rate=100) # 100 Hz physics simulation\nnodes = dict(sensor=sensor, agent=agent, actuator=actuator, world=world)\n\nsensor.connect(world, delay_dist=Normal(0.001, 0.001)) # Sensor delay\nagent.connect(sensor, delay_dist=Normal(0.001, 0.001)) # Communication delay\nactuator.connect(agent, delay_dist=Normal(0.001, 0.001)) # Communication delay\nworld.connect(actuator, delay_dist=Normal(0.001, 0.001), # Actuator delay\n skip=True) # Breaks algebraic loop in the graph\ngraph = AsyncGraph(nodes, agent,\n clock=Clock.SIMULATED, # Simulates based on delay_dist\n real_time_factor=RealTimeFactor.FAST_AS_POSSIBLE)\n\ngraph_state = graph.init() # Initial states of all nodes\ngraph.warmup(graph_state) # Jit-compiles the graph\nfor _ in range(100): # Run the graph for 100 steps\n graph_state = graph.run(graph_state) # Run for one step\ngraph.stop() # Stop asynchronous nodes\ndata = graph.get_record() # Get recorded data from the graph\n```\nNodes are defined using JAX's [PyTrees](https://jax.readthedocs.io/en/latest/pytrees.html):\n```python\nfrom rex.node import BaseNode\n\nclass Agent(BaseNode):\n def init_params(self, rng=None, graph_state=None):\n return SomePyTree(a=..., b=...)\n\n def init_state(self, rng=None, graph_state=None):\n return SomePyTree(x1=..., x2=...)\n\n def init_output(self, rng=None, graph_state=None):\n return SomePyTree(y1=..., y2=...)\n \n # Jit-compiled via graph.warmup for faster execution\n def step(self, step_state): # Called at Node's rate\n ss = step_state # Shorten name\n # Read params, and current state\n params, state = ss.params, ss.state\n # Current episode, sequence, timestamp\n eps, seq, ts = ss.eps, ss.seq, ss.ts\n # Grab the data, and I/O timestamps\n cam = ss.inputs[\"sensor\"] # Received messages \n cam.data, cam.ts_send, cam.ts_recv\n ... # Some computation for new_state, output\n new_state = SomePyTree(x1=..., x2=...)\n output = SomePyTree(y1=..., y2=...)\n # Update step_state for next step call\n new_ss = ss.replace(state=new_state)\n return new_ss, output # Sends output\n```\n\n## Next steps\nIf this quick start has got you interested, then have a look at the [sim2real.ipynb](https://github.com/anonymous/rex/blob/master/examples/sim2real.ipynb) notebook for an example of a sim-to-real workflow using Rex.\n\n## Citation\n\nIf you are using rex for your scientific publications, please cite:\n\n```bibtex\n@article{anonymous2024rex,\n title={{REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation}},\n author={Anonymized},\n journal={OpenReview},\n year={2024}\n}\n```\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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