From b6a24a93d116db3f0dce31131a821506654529cd Mon Sep 17 00:00:00 2001
From: Rooholla-KhorramBakht <learn.kho@gmail.com>
Date: Fri, 5 Apr 2024 15:59:21 -0400
Subject: [PATCH] HysteresisContactDetector added

---
 Go2Py/estimation/__init__.py          |   0
 Go2Py/estimation/contact.py           |  14 ++
 Go2Py/robot/model.py                  |  13 +-
 examples/contact_detector.ipynb       | 192 +++++++++++++++++++++-----
 examples/fsm_sim.ipynb                |  12 +-
 examples/lowlevel_dds_interface.ipynb |  32 ++++-
 examples/walk_these_ways_sim.ipynb    | 115 ++++++++++++---
 7 files changed, 308 insertions(+), 70 deletions(-)
 create mode 100644 Go2Py/estimation/__init__.py
 create mode 100644 Go2Py/estimation/contact.py

diff --git a/Go2Py/estimation/__init__.py b/Go2Py/estimation/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Go2Py/estimation/contact.py b/Go2Py/estimation/contact.py
new file mode 100644
index 0000000..bad1480
--- /dev/null
+++ b/Go2Py/estimation/contact.py
@@ -0,0 +1,14 @@
+import numpy as np
+
+class HysteresisContactDetector:
+    def __init__(self, upper_limit, lower_limit):
+        self.upper_limit = upper_limit
+        self.lower_limit = lower_limit
+        self.contact_state = np.zeros(4)
+    
+    def update(self, contact_forces):
+        self.contact_state[np.where(contact_forces>self.upper_limit)[0]]=1
+        self.contact_state[np.where(contact_forces<self.lower_limit)[0]]=0
+
+    def getContactStates(self):
+        return self.contact_state
\ No newline at end of file
diff --git a/Go2Py/robot/model.py b/Go2Py/robot/model.py
index d0c328f..9533ef9 100644
--- a/Go2Py/robot/model.py
+++ b/Go2Py/robot/model.py
@@ -156,8 +156,8 @@ class Go2Model:
         self.Minv_ = pin.computeMinverse(self.robot.model, self.robot.data, q_)[self.dq_reordering_idx,:]
         self.Minv_ = self.Minv_[:,self.dq_reordering_idx]
         for ef_frame in self.ef_frames:
-            # J = self.robot.getFrameJacobian(self.robot.model.getFrameId(ef_frame), pin.ReferenceFrame.LOCAL_WORLD_ALIGNED)
-            J = self.robot.getFrameJacobian(self.robot.model.getFrameId(ef_frame), pin.ReferenceFrame.LOCAL)
+            J = self.robot.getFrameJacobian(self.robot.model.getFrameId(ef_frame), pin.ReferenceFrame.LOCAL_WORLD_ALIGNED)
+            # J = self.robot.getFrameJacobian(self.robot.model.getFrameId(ef_frame), pin.ReferenceFrame.LOCAL)
             self.ef_J_[ef_frame]=J[:, self.dq_reordering_idx]
 
     def getInfo(self):
@@ -173,4 +173,11 @@ class Go2Model:
             'nle':self.nle_,
             'g':self.g_,
             'J':self.ef_J_,
-        }
\ No newline at end of file
+        }
+    
+    def getGroundReactionForce(self, tau_est, body_acceleration=None):
+        if body_acceleration is None:   
+            grf = {key:np.linalg.pinv(self.ef_J_[key][:3,6:].T)@(tau_est.squeeze() - self.nle_[6:]) for key in self.ef_J_.keys()}
+        else:
+            raise NotImplementedError("Ground reaction force with body dynamics is not implemented")
+        return grf
\ No newline at end of file
diff --git a/examples/contact_detector.ipynb b/examples/contact_detector.ipynb
index cb0de85..968c4c9 100644
--- a/examples/contact_detector.ipynb
+++ b/examples/contact_detector.ipynb
@@ -1,13 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulated"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 349,
    "metadata": {},
    "outputs": [],
    "source": [
     "from Go2Py.sim.mujoco import Go2Sim\n",
     "from Go2Py.robot.model import Go2Model\n",
+    "from Go2Py.estimation.contact import HysteresisContactDetector\n",
     "import pinocchio as pin \n",
     "import numpy as np\n",
     "import time"
@@ -19,15 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = Go2Model()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "model = Go2Model()\n",
     "robot = Go2Sim()"
    ]
   },
@@ -37,7 +37,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "robot.step()"
+    "contactFilter  = HysteresisContactDetector(12, 35)"
    ]
   },
   {
@@ -46,60 +46,188 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "robot.standUpReset()\n",
-    "while True:\n",
+    "dataset = []\n",
+    "dataset_measured = []\n",
+    "dataset_contact_states = []\n",
+    "for _ in range(5000):\n",
     "    time.sleep(0.001)\n",
-    "    robot.setCommands(robot.standing_q, np.zeros(12), np.ones(12)*100, np.ones(12)*0.1, np.zeros(12))\n",
-    "    robot.step()    \n",
-    "    trans, quat = robot.getPose()\n",
     "    state = robot.getJointStates()\n",
-    "    quat = pin.Quaternion(np.hstack([quat[1:],quat[0]]))\n",
-    "    R = quat.matrix()\n",
+    "    quat = robot.getIMU()['quat']\n",
+    "    quat = np.hstack([quat[1:], quat[0]])\n",
+    "    R = pin.Quaternion(quat).matrix()\n",
     "    T = np.eye(4)\n",
     "    T[0:3,0:3] = R\n",
     "    vel = np.zeros(6)\n",
     "    model.update(state['q'], state['dq'],T,vel)\n",
+    "    F = model.getGroundReactionForce(state['tau_est'])\n",
+    "    contact_forces = np.array([-F[key][-1] for key in F.keys()])\n",
+    "    contactFilter.update(contact_forces)\n",
+    "    contact_state = contactFilter.getContactStates()\n",
+    "    dataset_contact_states.append(contact_state.copy())\n",
+    "    dataset.append(model.getGroundReactionForce(state['tau_est'])['FR_foot'])\n",
+    "    dataset_measured.append(robot.getFootContacts())\n",
+    "dataset_measured = np.vstack(dataset_measured)\n",
     "\n",
-    "    J = model.getInfo()['J']['FR_foot'][:,6:]\n",
-    "    nle = model.getInfo()['nle'][6:]\n",
-    "    # tau = (robot.data.qfrc_smooth+robot.data.qfrc_constraint)[6:]\n",
-    "    tau = state['tau_est'].squeeze()\n",
-    "    print(np.linalg.pinv(J.T)[0:3]@(tau - nle))"
+    "dataset = np.vstack(dataset)\n",
+    "dataset_contact_states = np.vstack(dataset_contact_states)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Real Robot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 297,
    "metadata": {},
    "outputs": [],
    "source": [
-    "question: What exactly is the difference between the local world aligned and local jacobians in the pinocchio? Why it fits my expectations for the FR3 and not the Go2?"
+    "from Go2Py.robot.interface.dds import GO2Real\n",
+    "from Go2Py.robot.model import Go2Model\n",
+    "import time\n",
+    "from Go2Py.sim.mujoco import Go2Sim\n",
+    "from Go2Py.robot.fsm import FSM\n",
+    "import numpy as np\n",
+    "import pinocchio as pin"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "robot.getJointStates()\n"
+    "robot = GO2Real(mode='lowlevel')\n",
+    "model = Go2Model()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'q': array([ 0.07208335,  0.71541172, -1.52541149, -0.04195726,  0.77687514,\n",
+       "        -1.53210962,  0.10532299,  0.72009933, -1.50478077, -0.04001439,\n",
+       "         0.66759801, -1.4734081 ]),\n",
+       " 'dq': array([ 0.02325314, -0.01162657,  0.0161761 ,  0.0155021 ,  0.0658839 ,\n",
+       "         0.01011006,  0.01162657, -0.00775105, -0.00404402,  0.01937762,\n",
+       "         0.0658839 , -0.06066037]),\n",
+       " 'ddq': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
+       " 'tau_est': array([ 2.49856639,  0.66793358,  7.06484079, -2.22644544,  0.02473828,\n",
+       "         6.44844532,  1.68220317,  0.5442422 ,  6.44844532, -2.54804301,\n",
+       "         1.06374609,  6.25878525]),\n",
+       " 'temperature': array([30., 28., 27., 28., 28., 27., 28., 28., 27., 30., 28., 27.])}"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state = robot.getJointStates()\n",
+    "state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
    "metadata": {},
    "outputs": [],
    "source": [
-    "J[3:,:].T"
+    "contactFilter  = HysteresisContactDetector(12, 35)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 348,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "dataset = []\n",
+    "dataset_measured = []\n",
+    "dataset_contact_states = []\n",
+    "for _ in range(5000):\n",
+    "    # time.sleep(0.001)\n",
+    "    state = robot.getJointStates()\n",
+    "    quat = robot.getIMU()['quat']\n",
+    "    quat = np.hstack([quat[1:], quat[0]])\n",
+    "    R = pin.Quaternion(quat).matrix()\n",
+    "    T = np.eye(4)\n",
+    "    T[0:3,0:3] = R\n",
+    "    vel = np.zeros(6)\n",
+    "    model.update(state['q'], state['dq'],T,vel)\n",
+    "    F = model.getGroundReactionForce(state['tau_est'])\n",
+    "    contact_forces = np.array([-F[key][-1] for key in F.keys()])\n",
+    "    contactFilter.update(contact_forces)\n",
+    "    contact_state = contactFilter.getContactStates()\n",
+    "    dataset_contact_states.append(contact_state.copy())\n",
+    "    dataset.append(model.getGroundReactionForce(state['tau_est'])['FR_foot'])\n",
+    "    dataset_measured.append(robot.getFootContacts())\n",
+    "dataset_measured = np.vstack(dataset_measured)\n",
+    "dataset = np.vstack(dataset)\n",
+    "dataset_contact_states = np.vstack(dataset_contact_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(-dataset[:,-1])\n",
+    "plt.plot(dataset_measured[:,0])\n",
+    "plt.legend(['estimated', 'sensor'])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fa7098bfa90>]"
+      ]
+     },
+     "execution_count": 327,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(dataset_contact_states[:,0])"
+   ]
   }
  ],
  "metadata": {
@@ -118,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,
diff --git a/examples/fsm_sim.ipynb b/examples/fsm_sim.ipynb
index c247e1b..de27767 100644
--- a/examples/fsm_sim.ipynb
+++ b/examples/fsm_sim.ipynb
@@ -26,17 +26,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "controller timeout\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "robot.sitDownReset()\n",
     "fsm = FSM(robot, remote, safety_hypervisor)"
diff --git a/examples/lowlevel_dds_interface.ipynb b/examples/lowlevel_dds_interface.ipynb
index 7b7df0c..0ffb68b 100644
--- a/examples/lowlevel_dds_interface.ipynb
+++ b/examples/lowlevel_dds_interface.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pygame 2.5.2 (SDL 2.28.2, Python 3.8.18)\n",
+      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
+     ]
+    }
+   ],
    "source": [
     "from Go2Py.robot.interface.dds import GO2Real\n",
     "import time\n",
@@ -13,17 +22,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = robot.getJointStates()['q']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import time\n",
-    "for i in range(1000):\n",
-    "    q = np.zeros(12) \n",
+    "for i in range(1000000):\n",
+    "    # q = np.zeros(12) \n",
     "    dq = np.zeros(12)\n",
-    "    kp = np.zeros(12)\n",
-    "    kd = np.zeros(12)\n",
+    "    kp = np.ones(12)*20\n",
+    "    kd = np.ones(12)*0.3\n",
     "    tau = np.zeros(12)\n",
     "    tau[0] = -0.0\n",
     "    robot.setCommands(q, dq, kp, kd, tau)\n",
diff --git a/examples/walk_these_ways_sim.ipynb b/examples/walk_these_ways_sim.ipynb
index c686ed6..471fbc6 100644
--- a/examples/walk_these_ways_sim.ipynb
+++ b/examples/walk_these_ways_sim.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,9 +45,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p_gains: [20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20.]\n"
+     ]
+    }
+   ],
    "source": [
     "agent = WalkTheseWaysAgent(cfg, command_profile, robot)\n",
     "agent = HistoryWrapper(agent)"
@@ -55,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,9 +74,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "frq: 15.967047985229458 Hz\n",
+      "frq: 42.47353444522081 Hz\n",
+      "frq: 43.69977078558033 Hz\n",
+      "frq: 40.20806211954177 Hz\n"
+     ]
+    }
+   ],
    "source": [
     "robot.reset()\n",
     "obs = agent.reset()\n",
@@ -93,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,9 +179,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p_gains: [20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20.]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "frq: 0.10237921921805228 Hz\n",
+      "frq: 46.14348108298404 Hz\n",
+      "frq: 44.397794032030994 Hz\n",
+      "frq: 45.603148715941465 Hz\n",
+      "frq: 45.86595514341641 Hz\n",
+      "frq: 44.16591026356524 Hz\n",
+      "frq: 45.82636627843454 Hz\n",
+      "frq: 45.51950772169346 Hz\n"
+     ]
+    }
+   ],
    "source": [
     "checkpoint = \"/home/rstaion/projects/rooholla/locomotion/Go2Py/Go2Py/assets/checkpoints/walk_these_ways/\"\n",
     "# checkpoint = \"/home/rstaion/projects/rooholla/walk-these-ways/runs/gait-conditioned-agility/pretrain-v0/train/025417.456545\"\n",
@@ -172,18 +213,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "frq: 45.741406386320016 Hz\n",
+      "frq: 44.6901430961184 Hz\n",
+      "frq: 44.451434445775085 Hz\n",
+      "frq: 45.19042385846963 Hz\n",
+      "frq: 45.1087737411542 Hz\n",
+      "frq: 46.690013692073066 Hz\n",
+      "frq: 45.09228519824546 Hz\n",
+      "frq: 45.70053825535531 Hz\n",
+      "frq: 46.01388873651992 Hz\n",
+      "frq: 44.94924554183813 Hz\n",
+      "frq: 45.49383372200228 Hz\n",
+      "frq: 44.522684330085134 Hz\n",
+      "frq: 44.42130458266699 Hz\n",
+      "frq: 43.71525649844704 Hz\n",
+      "frq: 44.37383889464886 Hz\n"
+     ]
+    }
+   ],
    "source": [
     "controller.command_profile.pitch_cmd=0.0\n",
-    "controller.command_profile.body_height_cmd=0.1\n",
+    "controller.command_profile.body_height_cmd=0.0\n",
     "controller.command_profile.footswing_height_cmd=0.08\n",
     "controller.command_profile.roll_cmd=0.0\n",
     "controller.command_profile.stance_width_cmd=0.2\n",
     "controller.command_profile.x_vel_cmd=-0.2\n",
     "controller.command_profile.y_vel_cmd=0.01\n",
-    "controller.command_profile.setGaitType(\"pronking\")"
+    "controller.command_profile.setGaitType(\"trotting\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "controller.command_profile.pitch_cmd=0.0\n",
+    "controller.command_profile.body_height_cmd=0.0\n",
+    "controller.command_profile.footswing_height_cmd=0.08\n",
+    "controller.command_profile.roll_cmd=0.0\n",
+    "controller.command_profile.stance_width_cmd=0.2\n",
+    "controller.command_profile.x_vel_cmd=-0.2\n",
+    "controller.command_profile.y_vel_cmd=0.01\n",
+    "controller.command_profile.setGaitType(\"trotting\")"
    ]
   },
   {