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Update Franka Stacking Env (isaac-sim#146)
# Description <!-- Thank you for your interest in sending a pull request. Please make sure to check the contribution guidelines. Link: https://isaac-sim.github.io/IsaacLab/source/refs/contributing.html --> Update the Franka Stacking Env to align with Robosuite stacking task used in MimicGen. <!-- As a practice, it is recommended to open an issue to have discussions on the proposed pull request. This makes it easier for the community to keep track of what is being developed or added, and if a given feature is demanded by more than one party. --> ## Type of change <!-- As you go through the list, delete the ones that are not applicable. --> - New feature (non-breaking change which adds functionality) ## Screenshots Please attach before and after screenshots of the change if applicable. <!-- Example: | Before | After | | ------ | ----- | | _gif/png before_ | _gif/png after_ | To upload images to a PR -- simply drag and drop an image while in edit mode and it should upload the image directly. You can then paste that source into the above before/after sections. --> ## Checklist - [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with `./isaaclab.sh --format` - [ ] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [ ] I have added tests that prove my fix is effective or that my feature works - [x] I have updated the changelog and the corresponding version in the extension's `config/extension.toml` file - [ ] I have added my name to the `CONTRIBUTORS.md` or my name already exists there <!-- As you go through the checklist above, you can mark something as done by putting an x character in it For example, - [x] I have done this task - [ ] I have not done this task --> --------- Signed-off-by: peterd-NV <[email protected]> Co-authored-by: CY Chen <[email protected]> Co-authored-by: oahmednv <[email protected]> Co-authored-by: Toni-SM <[email protected]>
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...ns/omni.isaac.lab_tasks/omni/isaac/lab_tasks/manager_based/manipulation/stack/__init__.py
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....isaac.lab_tasks/omni/isaac/lab_tasks/manager_based/manipulation/stack/config/__init__.py
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...lab_tasks/omni/isaac/lab_tasks/manager_based/manipulation/stack/config/franka/__init__.py
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...ni/isaac/lab_tasks/manager_based/manipulation/stack/config/franka/stack_ik_rel_env_cfg.py
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...mni.isaac.lab_tasks/omni/isaac/lab_tasks/manager_based/manipulation/stack/mdp/__init__.py
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...ab_tasks/omni/isaac/lab_tasks/manager_based/manipulation/stack/mdp/franka_stack_events.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,33 +1,128 @@ | ||
| # Copyright (c) 2024, The Isaac Lab Project Developers. | ||
| # Copyright (c) 2022-2024, The Isaac Lab Project Developers. | ||
| # All rights reserved. | ||
| # | ||
| # SPDX-License-Identifier: BSD-3-Clause | ||
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| from __future__ import annotations | ||
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| import math | ||
| import random | ||
| import torch | ||
| from typing import TYPE_CHECKING | ||
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| from omni.isaac.lab.assets import AssetBase | ||
| import omni.isaac.lab.utils.math as math_utils | ||
| from omni.isaac.lab.assets import Articulation, AssetBase | ||
| from omni.isaac.lab.managers import SceneEntityCfg | ||
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| if TYPE_CHECKING: | ||
| from omni.isaac.lab.envs import ManagerBasedEnv | ||
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| def randomize_scene_lighting_domelight( | ||
| def set_default_joint_pose( | ||
| env: ManagerBasedEnv, | ||
| env_ids: torch.Tensor, | ||
| intensity_range: tuple[float, float], | ||
| default_pose: torch.Tensor, | ||
| asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), | ||
| ): | ||
| # Set the default pose for robots in all envs | ||
| asset = env.scene[asset_cfg.name] | ||
| asset.data.default_joint_pos = torch.tensor(default_pose, device=env.device).repeat(env.num_envs, 1) | ||
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| def randomize_joint_by_gaussian_offset( | ||
| env: ManagerBasedEnv, | ||
| env_ids: torch.Tensor, | ||
| mean: float, | ||
| std: float, | ||
| asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), | ||
| ): | ||
| asset: Articulation = env.scene[asset_cfg.name] | ||
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| # Add gaussian noise to joint states | ||
| joint_pos = asset.data.default_joint_pos[env_ids].clone() | ||
| joint_vel = asset.data.default_joint_vel[env_ids].clone() | ||
| joint_pos += math_utils.sample_gaussian(mean, std, joint_pos.shape, joint_pos.device) | ||
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| # Clamp joint pos to limits | ||
| joint_pos_limits = asset.data.soft_joint_pos_limits[env_ids] | ||
| joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1]) | ||
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| # Don't noise the gripper poses | ||
| joint_pos[env_ids, -2:] = asset.data.default_joint_pos[env_ids, -2:] | ||
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| # Set into the physics simulation | ||
| asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) | ||
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| def randomize_scene_lighting_domelight( | ||
| env: ManagerBasedEnv, | ||
| env_ids: torch.Tensor, | ||
| intensity_range: tuple[float, float], | ||
| asset_cfg: SceneEntityCfg = SceneEntityCfg("light"), | ||
| ): | ||
| asset: AssetBase = env.scene[asset_cfg.name] | ||
| light_prim = asset.prims[0] | ||
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| # Sample new light intensity | ||
| new_intensity = random.uniform(intensity_range[0], intensity_range[1]) | ||
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| # Set light intensity to light prim | ||
| intensity_attr = light_prim.GetAttribute("inputs:intensity") | ||
| intensity_attr.Set(new_intensity) | ||
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| def randomize_object_pose( | ||
| env: ManagerBasedEnv, | ||
| env_ids: torch.Tensor, | ||
| asset_cfgs: list[SceneEntityCfg], | ||
| min_separation: float = 0.0, | ||
| pose_range: dict[str, tuple[float, float]] = {}, | ||
| max_sample_tries: int = 5000, | ||
| ): | ||
| if env_ids is None: | ||
| return | ||
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| range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] | ||
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| # Randomize poses in each environment independently | ||
| for cur_env in env_ids.tolist(): | ||
| pose_list = [] | ||
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| # Randomize pose for each object | ||
| for i in range(len(asset_cfgs)): | ||
| asset_cfg = asset_cfgs[i] | ||
| asset = env.scene[asset_cfg.name] | ||
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| # Sample a pose | ||
| for j in range(max_sample_tries): | ||
| sample = [random.uniform(range[0], range[1]) for range in range_list] | ||
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| # Accept pose if it is the first one, or if reached max num tries | ||
| if len(pose_list) == 0 or j == max_sample_tries - 1: | ||
| pose_list.append(sample) | ||
| # Write pose to simulation | ||
| pose_tensor = torch.tensor([pose_list[i]], device=env.device) | ||
| positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] | ||
| orientations = math_utils.quat_from_euler_xyz( | ||
| pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5] | ||
| ) | ||
| asset.write_root_pose_to_sim( | ||
| torch.cat([positions, orientations], dim=-1), env_ids=torch.tensor([cur_env], device=env.device) | ||
| ) | ||
| break | ||
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| # Check if pose of object of sufficiently far away from all other objects | ||
| separation_check = [math.dist(sample[:3], pose[:3]) > min_separation for pose in pose_list] | ||
| if False not in separation_check: | ||
| pose_list.append(sample) | ||
| # Write pose to simulation | ||
| pose_tensor = torch.tensor([pose_list[i]], device=env.device) | ||
| positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] | ||
| orientations = math_utils.quat_from_euler_xyz( | ||
| pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5] | ||
| ) | ||
| asset.write_root_pose_to_sim( | ||
| torch.cat([positions, orientations], dim=-1), env_ids=torch.tensor([cur_env], device=env.device) | ||
| ) | ||
| break |
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