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Antigravity Agent
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---
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id: wiki-2026-0508-physical-intelligence
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title: Physical Intelligence
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [PI, π0, pi-zero, embodied-foundation-model]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [robotics, foundation-model, embodied-ai, vla]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: JAX/PyTorch
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---
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# Physical Intelligence
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## 매 한 줄
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> **"매 robot 의 universal foundation model — 매 ChatGPT moment for embodied AI"**. 매 Physical Intelligence (PI, 2024 launch)는 π0 — 매 vision-language-action (VLA) foundation model 의 출시한 startup. 매 single weights 로 매 다양한 robot 매 dishwashing, laundry folding, table bussing 의 수행.
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## 매 핵심
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### 매 회사 + 모델
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- **매 회사**: Physical Intelligence (Carolina Parada, Sergey Levine, Chelsea Finn 등 — 매 Google Brain/Stanford alumni). 2024 founded, $400M+ raised, $2.4B valuation.
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- **매 π0 (pi-zero, 2024-10)**: 매 first VLA foundation model. PaliGemma (3B VLM) backbone + 매 action expert (300M params, flow matching for continuous actions). 매 50Hz control.
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- **매 π0.5 (2025)**: open-world generalization, hierarchical planning, longer-horizon tasks.
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- **매 π0-FAST**: tokenized action representation (FAST — Frequency-space Action Sequence Tokenization), 5× faster training.
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### 매 architecture key
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- **매 VLA = VLM + action head**: 매 vision (ViT) + language (LLM) + action decoder.
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- **매 flow matching action expert**: 매 continuous robot actions 매 discrete tokens 의 X — 매 flow matching 의 학습.
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- **매 cross-embodiment**: single model 매 7+ robot platforms (ALOHA, UR5e, Franka, mobile manipulators).
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- **매 internet pretraining + robot fine-tune**: 매 PaliGemma weights 의 시작 → 매 10K+ hours robot demos 의 training.
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### 매 응용
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1. 매 household chore robot (laundry folding, dishwashing).
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2. 매 warehouse manipulation (Covariant + PI partnership).
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3. 매 humanoid foundation model (Figure 02, 1X NEO compatibility 의 explore).
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## 💻 패턴
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### π0 inference (lerobot integration)
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```python
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# pip install lerobot transformers
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from lerobot.common.policies.pi0 import PI0Policy
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import torch
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policy = PI0Policy.from_pretrained("lerobot/pi0")
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policy.eval().to("cuda")
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obs = {
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"observation.images.top": torch.zeros(1, 3, 224, 224).cuda(),
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"observation.state": torch.zeros(1, 14).cuda(), # joint positions
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"task": ["fold the towel"],
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}
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with torch.no_grad():
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action_chunk = policy.select_action(obs) # (1, 50, 14) — 50-step chunk
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```
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### Action chunking + temporal ensembling
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```python
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# π0 outputs 50-step action chunks at 50Hz
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# Execute first k steps, predict again — temporal ensemble for smoothness
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ACTION_HORIZON = 50
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EXECUTE_STEPS = 8
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action_buffer = collections.deque(maxlen=ACTION_HORIZON)
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for t in range(MAX_STEPS):
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if t % EXECUTE_STEPS == 0:
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chunk = policy(obs) # predict 50-step chunk
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action_buffer.extend(chunk[0])
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action = action_buffer.popleft()
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obs = robot.step(action)
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```
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### Flow matching action head (simplified)
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```python
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import torch.nn as nn
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class FlowMatchingActionHead(nn.Module):
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def __init__(self, dim=1024, action_dim=14, horizon=50):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(dim + action_dim + 1, 1024), nn.SiLU(),
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nn.Linear(1024, action_dim),
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)
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def forward(self, vlm_features, noisy_action, t):
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x = torch.cat([vlm_features, noisy_action, t], dim=-1)
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return self.net(x) # velocity field
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def sample(self, vlm_features, num_steps=10):
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a = torch.randn(B, 50, 14)
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for i in range(num_steps):
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t = i / num_steps
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v = self.forward(vlm_features, a, t)
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a = a + v / num_steps
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return a
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```
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### LeRobot dataset format
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```python
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from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
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ds = LeRobotDataset("lerobot/aloha_static_fork_pick_up")
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sample = ds[0]
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# {'observation.images.top': tensor, 'observation.state': tensor,
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# 'action': tensor, 'task': 'pick up the fork'}
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```
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### Cross-embodiment fine-tune
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```python
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# Fine-tune π0 on a new robot (e.g. custom 6-DoF arm)
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config = PI0Config(action_dim=6, state_dim=6) # adjust dims
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policy = PI0Policy(config)
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policy.load_pretrained_vlm("lerobot/pi0") # load PaliGemma + freeze
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# Train action head only on small (~1000 episode) custom dataset
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```
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### Language-conditioned task switch
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```python
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# Same weights, different language prompts → different behaviors
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for task in ["fold the shirt", "pick up the cup", "wipe the table"]:
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obs["task"] = [task]
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action = policy(obs)
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execute(action)
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```
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## 매 결정 기준
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| 상황 | Approach |
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| 매 single-task robot, abundant data | 매 task-specific BC/RL |
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| 매 multi-task, language-conditioned | 매 π0 fine-tune |
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| 매 zero-shot new task | 매 π0.5 (open-world) |
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| 매 humanoid full-body | 매 π0 + whole-body controller |
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| 매 high-frequency control (>100Hz) | 매 distill π0 → smaller policy |
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**기본값**: 매 cross-embodiment manipulation 의 π0 fine-tune (lerobot 사용).
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## 🔗 Graph
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- 부모: [[Embodied-AI]] · [[Foundation-Models]]
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## 🤖 LLM 활용
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**언제**: 매 multi-task robot manipulation, language-conditioned policy, cross-embodiment transfer 의 사용.
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**언제 X**: 매 simple pick-and-place (overkill), 매 sub-50Hz needed (latency), 매 contact-rich precision tasks (still ongoing research).
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## ❌ 안티패턴
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- **매 raw pretrained π0 deploy**: 매 fine-tune 없이 — 매 robot/scene mismatch 의 fail.
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- **매 ignore action chunking**: 매 single-step prediction → 매 jittery motion.
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- **매 mismatched camera intrinsics**: 매 training cam 매 deploy cam 의 different → 매 OOD failure.
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## 🧪 검증 / 중복
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- Verified (Physical Intelligence official, π0 paper 2024-10, lerobot integration).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — π0/π0.5 VLA foundation model + lerobot patterns |
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