README.md

RoboTwin2 Checkpoints

ACT, and pi0.5 single-task finetuning using B200 GPU on RoboTwin2.0 dataset.

The policies were trained on the following Tasks:

• place_phone_stand
• place_a2b_left
• move_can_pot
• handover_block
• put_bottles_dustbin

Data

• Demonstrations: 50 demo_clean episodes per task
• Embodiment: aloha-agilex (dual-arm)
• Action dim: 14 (6 DOF × 2 arms + 2 grippers)
• Cameras: cam_high, cam_right_wrist, cam_left_wrist

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ACT (Action Chunking Transformers)

Architecture

Param	Value
Backbone	ResNet-18
Hidden dim	512
Feedforward dim	3200
Attention heads	8
Encoder layers	4
Decoder layers	7
Chunk size	50
KL weight	10
Action dim	14
Dropout	0.1
Parameters	~83.9M

Training

Param	Value
Batch size	8
Epochs	6000
Learning rate	1e-5
LR backbone	1e-5
Weight decay	1e-4
Optimizer	AdamW
Save freq	every 2000 epochs

Checkpoints

Path	Seed	Val Loss
ACT/act-place_phone_stand/demo_clean-50/	0	—
ACT/act-place_phone_stand-run2/demo_clean-50/	1	0.038
ACT/act-place_a2b_left/demo_clean-50/	0	—
ACT/act-place_a2b_left-run2/demo_clean-50/	1	0.059
ACT/act-move_can_pot/demo_clean-50/	0	—
ACT/act-move_can_pot-run2/demo_clean-50/	1	0.036
ACT/act-handover_block-run2/demo_clean-50/	1	0.030
ACT/act-put_bottles_dustbin-run2/demo_clean-50/	1	0.032

Each checkpoint directory contains:

• policy_best.ckpt — best validation loss checkpoint
• policy_last.ckpt — final epoch checkpoint
• policy_epoch_{2000,4000,5000,6000}_seed_{0,1}.ckpt — intermediate checkpoints
• dataset_stats.pkl — normalization statistics

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Pi0.5 LoRA (place_phone_stand only)

Fine-tuned from gs://openpi-assets/checkpoints/pi05_base/params using the openpi framework.

Architecture

Param	Value
Base model	Pi0.5 (3B params)
PaliGemma variant	gemma_2b_lora
Action expert variant	gemma_300m_lora
Fine-tuning method	LoRA

Training

Param	Value
Batch size	32
Total steps	20,000 (trained to 9,000)
Save interval	200 steps
XLA memory fraction	0.45 (64 GB pool on H200)
GPU	NVIDIA H200 (143 GB VRAM)

Checkpoints

Path	Step
pi05_lora/place_phone_stand/step_5000/	5,000
pi05_lora/place_phone_stand/step_9000/	9,000

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Environment

• Framework: RoboTwin2.0
• Simulator: SAPIEN with Vulkan rendering
• GPU: NVIDIA H200 SXM (143 GB VRAM)
• CUDA: 12.8