Robot appears differently in window rendering vs offscreen rendering from camera #30
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I suspect there is something going on with one of the wrappers you are using for training with Surreal. The collision mesh is being rendered currently. You can change this by making sure this parameter is False - see this. |
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Dec 1, 2022
# 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,
I am trying to generate camera observations of rollouts of a policy trained with Surreal.
However I found that the Sawyer robot rendered with env.unwrapped.render() onscreen and the observation rendered with env.unwrapped.sim.render() look different. More specifically, the one rendered in the simulation window has more details whereas the one rendered in offscreen mode does not resemble the real look of the robot:
Is there a way to obtain the more realist looking robot in observation images?
Thanks
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