Instructions to use ZibinDong/ActionCodec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZibinDong/ActionCodec2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZibinDong/ActionCodec2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,679 Bytes
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config_version: 1
physical:
input_dim: 7
components:
translation_m:
dims:
start: 0
stop: 3
space: additive
input_order: 0
primitive_order: 2
input_transform:
scale: 1.0
clip: false
constraints:
order_0:
min: -2.0
max: 2.0
order_2:
min: -0.025
max: 0.025
order_1:
min: -0.08
max: 0.08
distortion_limit: 0.005
primitive_selection: smooth
orientation_so3:
dims:
start: 3
stop: 6
space: so3
input_order: 0
primitive_order: 2
input_transform:
scale: 1.0
clip: false
constraints:
order_0:
min: -3.141592653589793
max: 3.141592653589793
order_2:
min: -0.08
max: 0.08
order_1:
min: -0.25
max: 0.25
distortion_limit: 0.02
primitive_selection: smooth
representation: rotation_vector
multiplication: right
gripper_open_command:
dims:
- 6
space: additive
input_order: 0
primitive_order: 0
input_transform:
scale: 1.0
clip: true
constraints:
order_0:
min: 0.0
max: 1.0
distortion_limit: 0.5
primitive_selection: nearest
set_bpe:
lookahead: 8
allow_cross_component_merge: false
train:
primitives:
zero_primitive_components:
- translation_m
- orientation_so3
bpe:
spatial_vocab_size: 2048
vocab_size: 4096
quality_control:
hard_order_1_scale: 3.0
min_segment_length: 2
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