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+---
+id: wiki-2026-0508-proprioception
+title: Proprioception
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Body Awareness, Kinesthesia, Sixth Sense]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [neuroscience, sensorimotor, robotics, embodied-ai]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: ROS2/MuJoCo
+---
+
+# Proprioception
+
+## 매 한 줄
+> **"매 body 의 self-knowledge — joint angle, muscle tension, limb position"**. 1906 Sherrington 이 명명. 매 sixth sense — vision/hearing 없어도 매 finger 의 코 의 touch 가능. 매 2026 robotics + embodied AI 에서 매 proprioceptive observation 의 핵심 input.
+
+## 매 핵심
+
+### 매 receptors (생물)
+- **Muscle spindles**: 매 stretch + velocity 감지. Ia/II afferent.
+- **Golgi tendon organs**: 매 muscle force 감지. Ib afferent.
+- **Joint receptors**: 매 angle, end-range.
+- **Cutaneous mechanoreceptors**: 매 skin stretch — proprioception 의 augment.
+- **Vestibular system**: 매 head orientation — 매 proprioception 과 fuse.
+
+### 매 processing
+- 매 spinal reflex (knee jerk) → cerebellum (timing) → parietal cortex (body schema).
+- 매 sensorimotor integration: 매 efference copy + reafference 의 비교.
+- 매 body schema: 매 dynamic internal model — 매 phantom limb 의 disruption.
+
+### 매 응용
+1. Robotics — 매 joint encoder + IMU = robot proprioception.
+2. Embodied AI / RL — 매 proprioceptive observation vector.
+3. Rehabilitation — 매 stroke / Parkinson's.
+4. VR / haptics — 매 mismatch 의 motion sickness.
+5. Sports / dance training.
+
+## 💻 패턴
+
+### Robot proprioceptive state (MuJoCo)
+```python
+import mujoco
+import numpy as np
+
+model = mujoco.MjModel.from_xml_path("humanoid.xml")
+data = mujoco.MjData(model)
+
+def proprio_obs(data):
+    return np.concatenate([
+        data.qpos[7:],          # joint positions (skip root)
+        data.qvel[6:],          # joint velocities
+        data.sensordata,        # IMU, touch, force
+    ])
+```
+
+### RL with proprioception only (no vision)
+```python
+import gymnasium as gym
+import torch.nn as nn
+
+# 매 proprioceptive policy — 매 vision 없이 locomotion 가능
+class ProprioPolicy(nn.Module):
+    def __init__(self, obs_dim, act_dim):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(obs_dim, 256), nn.ELU(),
+            nn.Linear(256, 256), nn.ELU(),
+            nn.Linear(256, act_dim),
+        )
+    def forward(self, obs):
+        return self.net(obs)
+```
+
+### Sim-to-real proprioception (domain rand)
+```python
+# 매 real robot 의 sensor noise 의 simulate
+def add_proprio_noise(qpos, qvel):
+    qpos = qpos + np.random.normal(0, 0.01, qpos.shape)  # encoder noise
+    qvel = qvel + np.random.normal(0, 0.05, qvel.shape)
+    # latency
+    return delay_buffer.push_pop(np.concatenate([qpos, qvel]))
+```
+
+### IMU sensor fusion (Madgwick)
+```python
+import numpy as np
+
+class Madgwick:
+    def __init__(self, beta=0.1, dt=0.01):
+        self.q = np.array([1.0, 0, 0, 0])
+        self.beta = beta; self.dt = dt
+
+    def update(self, gyro, accel):
+        # 매 gyro integration + accel correction
+        # (매 simplified — 매 full impl 의 quaternion math)
+        q = self.q
+        qdot = 0.5 * quat_mul(q, np.array([0, *gyro]))
+        # accel-based gradient correction omitted for brevity
+        self.q = (q + qdot * self.dt)
+        self.q /= np.linalg.norm(self.q)
+        return self.q
+```
+
+### Body schema (forward model)
+```python
+class ForwardModel(nn.Module):
+    """예측: q_t, action_t → q_{t+1}, expected_proprio"""
+    def __init__(self, q_dim, a_dim):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(q_dim + a_dim, 128), nn.ReLU(),
+            nn.Linear(128, q_dim))
+    def forward(self, q, a):
+        return q + self.net(torch.cat([q, a], dim=-1))
+
+# efference copy: prediction error = surprise signal
+```
+
+### Wearable proprioception (rehab)
+```python
+# IMU on each segment → joint angle estimate
+def joint_angle(imu_proximal, imu_distal):
+    q_p = imu_proximal.quaternion
+    q_d = imu_distal.quaternion
+    q_rel = quat_mul(quat_conj(q_p), q_d)
+    return quat_to_euler(q_rel)
+```
+
+### Embodied AI (humanoid robot, 2026)
+```python
+# 매 Figure / Tesla Optimus / Unitree style
+# 매 proprioception 27-dim + vision 의 multimodal policy
+obs = {
+    "proprio": robot.read_proprio(),   # 27-dim
+    "image": camera.read(),            # 224x224x3
+    "language": "pick up the red cup",
+}
+action = policy(obs)  # diffusion policy or transformer
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Locomotion RL | **Proprio-only** (fast, robust) |
+| Manipulation | Proprio + vision |
+| Outdoor terrain | Proprio + IMU + vision |
+| Whole-body humanoid | Proprio + vision + language |
+| Rehabilitation feedback | Proprio + EMG |
+
+**기본값**: Joint encoder + IMU + foot contact = baseline robot proprioception.
+
+## 🔗 Graph
+- 부모: [[Embodied-AI]]
+- 변형: [[Kinesthesia]]
+
+## 🤖 LLM 활용
+**언제**: 매 robotics task design, 매 obs space spec, 매 sim-to-real noise model.
+**언제 X**: 매 quantitative neuroscience claim — 매 paper citation 의 필수.
+
+## ❌ 안티패턴
+- **Sim에서만 perfect proprio**: 매 real 의 noise/latency/drift 가 policy 의 break. 매 domain randomize.
+- **Vision-only locomotion**: 매 occlusion / dark 의 brittle. 매 proprio fallback 필수.
+- **No efference copy**: 매 forward model 의 학습 의 unstable. 매 action 의 condition.
+- **Ignoring foot contact**: 매 binary contact signal 의 매 locomotion policy 의 critical input.
+
+## 🧪 검증 / 중복
+- Verified (Sherrington 1906, Proske & Gandevia 2012, modern RL robotics literature).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — proprioception biology + robotics application. |