How can I move the objects as I wish in this environment? #29
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You can see this comment here, which explains that the range of the uniform random initializer is specific to the center of the table. So one way to control object placement is just to write your own placement initializer. Basically, every environment owns a task which specifies the model (for example, https://github.com/StanfordVL/robosuite/blob/master/robosuite/environments/sawyer_lift.py#L163), and the task uses the initializer to place objects (for example, here). |
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# robosuite 1.4.0 Release Notes - Highlights - New Features - Improvements - Critical Bug Fixes - Other Bug Fixes # Highlights This release of robosuite refactors our backend to leverage DeepMind's new [mujoco](https://github.com/deepmind/mujoco) bindings. Below, we discuss the key details of this refactoring: ## Installation Now, installation has become much simpler, with mujoco being directly installed on Linux or Mac via `pip install mujoco`. Importing mujoco is now done via `import mujoco` instead of `import mujoco_py` ## Rendering The new DeepMind mujoco bindings do not ship with an onscreen renderer. As a result, we've implented an [OpenCV renderer](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/utils/opencv_renderer.py), which provides most of the core functionality from the original mujoco renderer, but has a few limitations (most significantly, no glfw keyboard callbacks and no ability to move the free camera). # Improvements The following briefly describes other changes that improve on the pre-existing structure. This is not an exhaustive list, but a highlighted list of changes. - Standardize end-effector frame inference (#25). Now, all end-effector frames are correctly inferred from raw robot XMLs and take into account arbitrary relative orientations between robot arm link frames and gripper link frames. - Improved robot textures (#27). With added support from DeepMind's mujoco bindings for obj texture files, all robots are now natively rendered with more accurate texture maps. - Revamped macros (#30). Macros now references a single macro file that can be arbitrarily specified by the user. - Improved method for specifying GPU ID (#29). The new logic is as follows: 1. If `render_device_gpu_id=-1`, `MUJOCO_EGL_DEVICE_ID` and `CUDA_VISIBLE_DEVICES` are not set, we either choose the first available device (usually `0`) if `macros.MUJOCO_GPU_RENDERING` is `True`, otherwise use CPU; 2. `CUDA_VISIBLE_DEVICES` or `MUJOCO_EGL_DEVICE_ID` are set, we make sure that they dominate over programmatically defined GPU device id. 3. If `CUDA_VISIBLE_DEVICES` and `MUJOCO_EGL_DEVICE_ID` are both set, then we use `MUJOCO_EGL_DEVICE_ID` and make sure it is defined in `CUDA_VISIBLE_DEVICES` - robosuite docs updated - Add new papers # Critical Bug Fixes - Fix Sawyer IK instability bug (#25) # Other Bug Fixes - Fix iGibson renderer bug (#21) ------- ## Contributor Spotlight We would like to introduce the newest members of our robosuite core team, all of whom have contributed significantly to this release! @awesome-aj0123 @snasiriany @zhuyifengzju
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Hi, thank you for providing a complete simulation robotic arm a lot.
However, now I want to get more different pictures about the state, I don't know how to move the objects?(e.g. bottles, mikes.)
I tried to modify the location of the random initialization, but the change was not obvious.
Perhaps, this project is too big to review all details especially my English is not skilled~
May I could not find your example programs?
Can you please provide one direction and let me go to learn how to move those, your project or mujoco-py?
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