mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-19 01:07:18 +00:00
Pepijn
63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from typing import Any
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import numpy as np
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import rerun as rr
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def _init_rerun(session_name: str = "lerobot_control_loop") -> None:
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"""Initializes the Rerun SDK for visualizing the control loop."""
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batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
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os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
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rr.init(session_name)
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memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
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rr.spawn(memory_limit=memory_limit)
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def log_rerun_data(observation: dict[str, Any], action: dict[str, Any]):
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for obs, val in observation.items():
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if isinstance(val, float):
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rr.log(f"observation.{obs}", rr.Scalar(val))
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elif isinstance(val, dict):
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# Handle dictionary of joint values
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for joint_name, joint_val in val.items():
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if isinstance(joint_val, (float, int)):
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rr.log(f"observation.{obs}.{joint_name}", rr.Scalar(float(joint_val)))
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elif isinstance(val, np.ndarray):
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if val.ndim == 1:
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for i, v in enumerate(val):
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rr.log(f"observation.{obs}_{i}", rr.Scalar(float(v)))
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else:
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rr.log(f"observation.{obs}", rr.Image(val), static=True)
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for act, val in action.items():
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if isinstance(val, float):
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rr.log(f"action.{act}", rr.Scalar(val))
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elif isinstance(val, dict):
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# Handle dictionary of joint values
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for joint_name, joint_val in val.items():
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if isinstance(joint_val, (float, int)):
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rr.log(f"action.{act}.{joint_name}", rr.Scalar(float(joint_val)))
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elif isinstance(val, np.ndarray):
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for i, v in enumerate(val):
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rr.log(f"action.{act}_{i}", rr.Scalar(float(v)))
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elif isinstance(val, list):
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# Handle list of values
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for i, v in enumerate(val):
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if isinstance(v, (float, int)):
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rr.log(f"action.{act}_{i}", rr.Scalar(float(v)))
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