fix
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3
.gitignore
vendored
3
.gitignore
vendored
@@ -1,3 +1,4 @@
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cobot_magic/
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librealsense/
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data*/
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data*/
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outputs/
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@@ -366,18 +366,18 @@ class AgilexRobot(Robot):
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from_idx = to_idx
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# Apply safety checks if configured
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if 'max_relative_target' in self.config:
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# Get current position from the queue
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if len(self.sync_arm_queues[arm_name]) > 0:
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current_state = self.sync_arm_queues[arm_name][-1]
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current_pos = np.array(current_state.position)
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# if 'max_relative_target' in self.config:
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# # Get current position from the queue
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# if len(self.sync_arm_queues[arm_name]) > 0:
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# current_state = self.sync_arm_queues[arm_name][-1]
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# current_pos = np.array(current_state.position)
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# Clip the action to stay within max relative target
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max_delta = self.config['max_relative_target']
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clipped_action = np.clip(arm_action,
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current_pos - max_delta,
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current_pos + max_delta)
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arm_action = clipped_action
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# # Clip the action to stay within max relative target
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# max_delta = self.config['max_relative_target']
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# clipped_action = np.clip(arm_action,
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# current_pos - max_delta,
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# current_pos + max_delta)
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# arm_action = clipped_action
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action_sent.append(arm_action)
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@@ -2,7 +2,7 @@ import argparse
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from common.rosrobot_factory import RobotFactory
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from common.utils.data_utils import record
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from common.utils.replay_utils import replay
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import cv2
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def get_arguments():
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"""
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@@ -2,7 +2,7 @@ from lerobot.common.policies.act.modeling_act import ACTPolicy
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from lerobot.common.robot_devices.utils import busy_wait
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import time
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import argparse
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from agilex_robot import AgilexRobot
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from common.agilex_robot import AgilexRobot
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import torch
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def get_arguments():
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@@ -29,12 +29,12 @@ def get_arguments():
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cfg = get_arguments()
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robot = AgilexRobot(config_file="/home/ubuntu/LYT/aloha_lerobot/collect_data/agilex.yaml", args=cfg)
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robot = AgilexRobot(config_file="/home/ubuntu/LYT/lerobot_aloha/lerobot_aloha/configs/agilex.yaml", args=cfg)
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inference_time_s = 360
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fps = 30
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device = "cuda" # TODO: On Mac, use "mps" or "cpu"
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ckpt_path = "/home/ubuntu/LYT/lerobot/outputs/train/act_move_tube_on_scale/checkpoints/last/pretrained_model"
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ckpt_path = "/home/ubuntu/LYT/lerobot_aloha/outputs/train/act_move_tube_on_scale/checkpoints/last/pretrained_model"
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policy = ACTPolicy.from_pretrained(ckpt_path)
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policy.to(device)
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