Add Stack Pyramid task and motionplanning solution #683
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Previously max_episode_steps configuration doesn't work because config["eval_env_cfg"] is set based on find(max_episode_steps), which isn't compatible for AsyncVectorEnv. Instead, assuming make_eval_envs make envs with the correct args.max_episode_steps, we can use args.max_episode_steps
…jectory Generation
StoneT2000
requested changes
Nov 7, 2024
| @@ -16,7 +16,7 @@ | |||
| @register_agent() | |||
| class Panda(BaseAgent): | |||
| uid = "panda" | |||
| urdf_path = f"{PACKAGE_ASSET_DIR}/robots/panda/panda_v2.urdf" | |||
| urdf_path = f"{PACKAGE_ASSET_DIR}/robots/panda/panda_v3.urdf" | |||
| def __init__( | ||
| self, *args, robot_uids="panda_wristcam", robot_init_qpos_noise=0.02, **kwargs | ||
| ): | ||
| self.num_cubes = 6 # 3 + 2 + 1 |
| z_offset = torch.abs(offset[..., 2] - self.cube_half_size[2]) | ||
| xy_flag = xy_offset <= 0.05 | ||
| z_flag = z_offset <= 0.05 | ||
| logger.debug(f"NEXT TO Distance XY: {xy_offset}") |
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remove the print statements and logging
| @@ -0,0 +1,69 @@ | |||
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we don't usually have per env specific tests. this should be removed
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Gotcha. As a side question, I'm curious if there's a reason environments generally don't have per-environment tests?
My rationale for unit testing environment implementation was that it could help ensure the environment works correctly with current and future code changes. However, among different RL environments / framework, I've only come across one instance of environment-specific testing in Gymnasium, specifically in this file:
| **Success Conditions:** | ||
| - the blue cube is static | ||
| - the blue cube is on top of both the red and green cube (to within half of the cube size) | ||
| - the blue cube is static |
…es, and reversed change for updating panda version
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Thanks for the comments! They should be addressed now, please take a look |
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