WIP
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@ -72,7 +72,7 @@ class TemporalQueue:
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nearest_idx = distances.argmin()
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# print(float(distances[nearest_idx]))
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if float(distances[nearest_idx]) > 1 / 5:
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if float(distances[nearest_idx]) > 1 / 30:
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raise ValueError()
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return self.items[nearest_idx], self.timestamps[nearest_idx]
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27
test2.py
27
test2.py
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@ -1,7 +1,10 @@
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import time
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import torch
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from lerobot.common.datasets.factory import make_dataset
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from lerobot.common.policies.factory import make_policy
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from lerobot.common.robot_devices.utils import busy_wait
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from lerobot.common.utils.utils import init_hydra_config, set_global_seed
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from tests.utils import DEFAULT_CONFIG_PATH
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@ -12,7 +15,7 @@ def main(env_name, policy_name, extra_overrides):
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overrides=[
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f"env={env_name}",
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f"policy={policy_name}",
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"device=cpu",
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"device=mps",
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]
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+ extra_overrides,
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)
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@ -31,15 +34,25 @@ def main(env_name, policy_name, extra_overrides):
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obs = {}
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for k in batch:
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if k.startswith("observation"):
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obs[k] = batch[k]
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obs[k] = batch[k].to("mps")
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actions = policy.inference(obs)
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# actions = policy.inference(obs)
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action, timestamp = policy.select_action(obs)
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fps = 30
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print(actions[0])
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print(action)
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for i in range(100):
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start_loop_t = time.perf_counter()
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next_action, timestamp = policy.select_action(obs)
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dt_s = time.perf_counter() - start_loop_t
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print(f"{i=}, {timestamp}, {dt_s * 1000:5.2f} ({1/ dt_s:3.1f}hz)") # , {next_action.mean().item()}")
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busy_wait(1 / fps - dt_s)
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# time.sleep(1/30) # frequency at which we receive a new observation (30 Hz = 0.03 s)
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# time.sleep(0.5) # frequency at which we receive a new observation (5 Hz = 0.2 s)
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if __name__ == "__main__":
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main("aloha", "act", ["policy.n_action_steps=10"])
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main("aloha", "act", ["policy.n_action_steps=100"])
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