Last Week’s Work Review#
Recall our Phase 1 visual module training scheme
Our Phase 2 language module training scheme
the testing accuracy could achieve $99.0%$ But I doubt about the true performance of this model.
- the model can achieve high accuracy by just memorising the current 3D grid value as long as the incremental change is small.
- which is true for this dataset because one instruction is often paired with a small change in the environment
- I should change the current training scheme to prevent model from taking shortcuts
- local addition
- one step (no intermediate states)
- local addition
After completing language module, our next step is to build PDDL model to bridge the gap between current state to the target state described by the instruction.
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2KL193ngiqg12yJiDH4bw+D2Nlm45qLF/v/qafJrxuBWcpeaKoACuOYOWAlIK5SiIofGzwRhcZOhHR3LtmKaM/j9J0y2vO9Z69ZqP4Ko3UkGLexL1+U7O0+yKZZB9Dw0NBdkewbMFlw00vKHbhtiw37xANvtixAYOeEERG9wDEZPCufYK0nH6DvqP7pzJh28eiAW5I5gKKaMPGGAslvygAU14z7zdpsqrXwajbgmt8F+Z/ivDwgux9KSFtt1azUe/G3LtXjZ2DV98FWXTX9rYA6yqvYFypTnAozyRodTgJ6XJ632eaBg0AYilHlCmk/mZLmoXiPfhenzNEoWOuuTlCv8rDB5x7P1Md5eWduAPPmyEmg3wRZ1vjkDARmudu1M1zczgQnsPwhM3TcMGHb50RGdL9KvqJT82X3e5I9wpBM6VmQ62/jq4X8FtREkU3SUcu8LST9htrkZzBnsLfvMts/VZriOclfCb9mDXuO3sJhixVdf1ygPrIQ3dEGkpAA1kWw1FF5EqJQynVAWOGNURUoKQjNB7FZzM+slz1TmWtZhEvLWDuOBhQWLp4zX9knz5eJ+Q1ZgMfljPEEkrVIcqDokag0kJT5eo0Pj8zUI0gPEAA5KYD0SzGwDuqbfJp6I3VIqZWm1xxpa4L4PhUvK05/av4pQAd14eOZkDP4Uliq+1fWS6aBoKtqvh/kafeVFFFpkahp+Zk5zsJUUvSRg56Ravg0SX39DZk8W1dt5b7vsd6+321tBWjDsmh7qT+cunQsYXVfJNv4AtQdWybA1sh4awn3OCsOsAXjnXx4wi0PVYz8DU6u/yZtHdRiS4Tfq7hB5hOq8QsatTEwo6J0DQSgc2QeVeyrPWRcYGJxt/y1vaxrJDdP+APPrvH+iN5sK+RIWLreOwfd9vO8aQwuWmWae5VacUZSwTkkbBfI0wMzvFfqcn1GfBo/3eQLUf+M/L3hgOxKbH9dQSf6BN0Z1klp8zrUzkd/49OCp/Z7ZAKn5UmiDtD02lWAMF+N1bjOU8ZToLYsoEWjtepbC7qQWhd2n7V3qbNoiEOdnXK4txiAvlYm9Sb+yWoQxM4ByfB25kWkRBMXhtCbZXwUTauZKk0r1XWyxA6Ew8qnW1V+X0Z2ktbwN540ik/qJYwF4qwWxT78zaLLGdp+Bd/Wjjyb2B3GkcctO+DKCNtJPcU6I43QaXgFjFe2eR4IAN6TlbT6wAVLh2f2S4R/IF+yVxlDXRFvPwoG7SoFr8lS+t5Uejg81F6FJvoLq/s5qCtP7Zf40oXM5ZlVg1coN4DWqKpqiUUmIMG4a3fA/yJyyZIh1ecjngguNAJNt9ZUSYal6ud+zUEHNFs/8u9sx4zk78XVzQZWFWFinJL7AQQg9iURLKxe12GmJJ+hFV3Cw4FjqEWXrkwrAT2Wg+REy75g4nzw7cy/Qg1GxI/WFj3OH32v0ayha45hztKoGpk3lvVvT11j+wYw9y5YU4qEkkSIjoLoONmsXKasH9Nxcjp7KxXfeaVGFetW6WAsA4DrChD0ztNJaG4RXXTEQd4+sUbcV+fi/bng2khyra823+GaIujMCNPnqtM7unLZYs3J3a47w69SokWLgi2hT+YAYvm8Fr4hzlGDCyBk01yR+wi0dtGEjMwzQJE18TJ5gpYH2i9wRWOvV/iUsYUJaSMp0S8JOjpqmzWqFFjWFVy2DMZuQ4isMQkdb4iKaTMTqiQ2GJHSPqSoQIj1ixyRsG4HEkAxrV1T7xAGcqPiWoNR5kI54othmAXot/y0NVvoB4Fqc4T4+i0MR1Gv0FwYYPPf2a25OtfNc1tDyK3ccPJlYvaakvLut7/A93JX9g+MGUCDJsUY/s4+FAqOXYR4LlYfppUfgJZIeIBJNze1mfbMs9f9AGJdshhn/BT5/wFd0xoV+sN69DookuT4uSKaOolT6xsW1j5QTWN7LvHdRIcxWRTlB/T1UAJvPL1gwZEE/kYyMv/hocYA6KrwaBwqS/cm4lCAuf4oJejfHlBxIeCVicEPkTNP7qp6TbS5QYIaB+jVPEDt2PgZD/6VFqv5StuHylbZE5SEUNUSYhRBUF0V9EuXbTjKGg3keg7xXv7dzEf65FX2oN/ov+64xW8KDiWtrz3QCJpt37bw7h5874GXzSm3X+Ju1dMd21I71KQKd0D4Uqj9i+q5qFyWddaxBB7M7tjFEqXilA0F+TkgYh2lfx+pPdqEYc4TFQ7P5seDTIvKBnLl7zUCiuVaimx56NM1lHzL0lVUH7t66DkWfOvhWiPphGUXYD+hskIYdjRcMEPuGpz2VyH6Ri5E6PQPaYO7dNGMQcJWOXF9kgSz/lgAfAJkOSjDVS4b2BTRoC7ESIeGejyvOpvlRfqs+S4QlpLqphypcv51aPVmj3E6At7UP2ayuw5wRRb0W0El1XU2kD1y2vVYBuaMj6yPGY2b3DN9f6BapxblIv2mgFvzpdMgzKtWWYG3sHqPP47N1wQcHrxHEixesn/LqUJhelhvLt2FOJPZny584vLSXAfqLclkdpNztkVi6v5pWaJl4MfpJmh8yTiRWkqU4PlGyGl8JBzKrXmteQvY5rNPdgWkjdYQYej7ECN6y7X4n+LguzjD2FF4Xr7ZgCIRyK+pYQZQpYEm8r+tB0TTfFpTdatwgDPmI1Fd09FVk0Nc8AQe0w2NF5ZGhicTFRDdmF+URXuorfqKT14iOxLXOdVpOIlCX1BU3ucArAyJVF5wgTX2i6lL1M38qWZCHESp0ltgztE2uLnIkxGvbUXoC3O0021gm2iIalV1nH7hLHdtDxQqcvBoA/zZQ+/5uJhVUrLovs3toxRS5UGT1v5bUmvV58YM61D2ZNsXX7N96V53RuL27pvko+VJ+fTEZf0D8WGIjPph5/5sb3cHaLLSTmWRmCjsn/UZj+PQBCaCYiZBQZ0C/IRojL/Q/1QJ1l5uPooNEBOGchGS6cT/vVO6LzrLbwX2Tnl1gL91zcUuw4kyx0hVMm45VTDsa3vQN/p8Am7ytMEmaK+6fEhUkS2vgybDRIvK0ruE8C1HXkkSkC+1NGrI/Eo1wyWCCwPsrQSKtkQzr2bcx637oMbNPEPNwmVywv2+zM/5dsofgpE5xiKf9I5UroH2zxuXf0MAg4+oW057yw1woeRqIZhilJ9gMZObf7HpxRS7er6MDdd32+J+uFzqR7byB2qX7L5VyXf7Tl7MLENuGBh7u+QTEe2/AzTbXrbkXqrOb1L7JVHf2TZjHcx+98xsI6Gg3NPhjmb73GEzQu0BC6gbBWZ7qrfM6XUBLngBVLYpGijV9z6DFBB4mkBZ0gnIpJsJSqmsx2QQcJGXlxtuD8vMPmjFup8tKROUorpgtYvmESUuQa9CtZp5LSlHAzoN/F8oEsnSc3a7vmYy+Wb5f0oNCPDJtfzhFV8ZDsVrPODrQ2j1t0V+OObp5SDB6reu2YP3GY9H5T+nXZvaIawVt+6E76AfQMkUTDULqBwKAd1Cb7tvFMSfnNWvMDaRngk28nfuAHah1aodUjblG75JCX+upepk+A0gLPhVYTCGf2DfZBePlXK/TOD719Fa/VEvks0Sv2uOWNxyrNHgADcNpqQv1UUXlkRWCDVB7G9N6Zjme1lEFEB8emsmQ3bWvVWvN+1CYgso6zFw/pk5UsJER/J5jor3s6ZS5DPbcwEiQ/wNuPskyoDwceVrHDFlUKRqNP2Q1sCx6PYbAOXXCrByMnqNYYK8iW+Mp0GeYvOnNqyGQIzpE0qRtX/A2cZLaS75LCOVSjeuz2HpePYTX0xfVjPPF2O8teahv9Djqz04K3BF2Y8awYadbRXxj/ZGpe+wAU0o8SYjJBu9fM6D5WpATWEPV0QknmU9ek5KAlytXEgJ24oLUJkHg95kl8OkvBmlMpKQ0CV6GXFbKs8uGp8V1eAMNdF7FPIdCNI1RswuFJyxy8sEAQNE9IsukmWugmBOfYvU3OVVraEsR3Xm2Ul6tVxhKjIUAehng7YGStTnIUA9HF3Km0LDzhxKefmV4yTcZDxz7lDGn58knP6wcLEGOaKXOXgf0C00O1DdZSvLnskXnZdI0lgyEF0Mu8gqyLQhTsFp3tRJ+rv6TBh/naJDawJoclXyaAJBMtdD0tHkJfkEvSYlmXjV09b22f1UnTwM34WkiXsZ7nYChiaXmRa/3hcwMtMErdKqbZRZRsM+BhGEcO4vx4oMlsPVutOszQKigT3zW4dePwppEG9Qr00/d/OQo+DE0jXo/D2fJSzSb7C4ESPHMPEaHqoJbq1td6WV6UfpvMCrsfTrphjrEGDVk7n1SLlV8j4WWuGh+JqKCPKCU9LMzMX0pOc9aJRwZitoUfhgl6lWcZMVwb/X9kAb9hL6iXoIk0afBEnt8gWLPqyAzfwUWeavJ4aQJJXvN5cVZmg+YTa0XdjDkFht8JH6xCNVcL6mBb7vAG5WRQzsKywKCZumrhctG6H38SO2XLS83D89sZs0EwQC6tT9ZVT1TeeLbkvLEfPp6MV4CeUhZnIY73iWb3fLfJC1W/iDmDmiQssuCzABteUOw5GEXJA/pbjeQa7yhegpyHNHsjKiIEV41ZzCB+0mZtL1PU51oFN0yCBGBkl7NiZgknJCLgHPWro0CSbOeYrePEfcMFGWqVatI34vXS9/bF7GTRkUqC/aSQtHfcs/TIr9OrwuS0ks3eRA+I+DZ+FA9V/2fw9hDTP+k+AoM0C9TCEuiDB7oshakAcd4sGzJTUtEOW88n7XAf6OViG+GTLVPBJ6od9b194O+9ZtXhiJ7HX/sN7qBKIQj7YFK/wRtiL+TfIgK+1ITJwvB7W+8qS7L3/IoUVYFZngYKXI9Z8Iq3Wv6cmlDkLSRQE3sgmZc2UfytxTaNaC7gez2LLmsiAwXOjj/JAFeE5sTwN27OCqbllbyS4tAjeAAqEp1l3tKi1sEDPH7trZ9Nl4vmYshDj5u20NmEJ2ULcaV2Ub6uc+OTphrFZSl9lbphYc/anEs7rkR5SJc6CwoGZBXLl46k7sV1pLqFP6H23aWZHONG+PvFawKIolRZsAVaFmeRAWMxgCS3mQSuxxU6zznejBwquf/9KKka/jydG3cVeA6Uqw0hClD3dgoVa4YkK/BIuDI7qnquOzjxmVs/j7D+gici6FyqjMe1N2cmylFmtaoxnVcmRCU9I3Qr1XLIUfd/8TNefDj5j66+iOBV90MOa8kfvbxyruNp8JB0fzpuoVbZADWZi5u6uQrSAZ73oC79PUxBKFhCgsNk9qrttV58p78Y+lSuRiiQ/LtisKdXKxU2QTt5KnTI+slY5C+ggza6dAWvsRcc6WLFe9UO1ZXMvtOxSfHGg3Ooh9lz+9evwlWPWNRn0XjelrRETiWBf8oH3cOmwU9VZoZs1jgYPQW98riMoJXtyyHCCrQ/OOtUV45MRejKrPySo+TTBcCfF5ZnR6T871rRGoAHdg0oWsJVditzRBxhSdaQD/HhHhDVakfwscwMs6HXcdudu1AuUxYkOEBXKUHN2H2a3Em4FbaPutuaItX3eZk7EzgQabW8xdQEr8kC4OVPh/VkI7WO6qukbd2n/zXwtwdC8Bto9CX+pqiv9zlW54WxJx6HWtdNRqCyy7KXT/uhODumB0LHyVcJTCJnEokRAETGrNjMzgBYh7RGvCFo0mLUx0WNH13aP+XkNEdKm+e0fka4f0FQ08XZL+wrUe4WL+49A7j+BdytdAb9RvQETR5UF+pY6FlnuIztbiOiiyehZ/v5+DOS9u42+rEIbyFgPogxPjBzJOJ2O8XryD3QAz4ZteWSDG/AH+E+jyO8boTKBCAj+3rPgWuyZjAQCxPjJY2hYQLuG1q9pbVbxaBJscoiMC+khX4tNpYz2NN++fJJyl4JZO359OlZqG272THkIMltrE6AjtUvcJjlTZTqQ5zuysz1WPBqRudqWghBUre1UNV/sbM7bteWNUBXvGRLExgh9Ve7CAvttkfksNY4A++4HTTRuZK3pk8q+/g7q+qS6vJsmbGExPpctSnnnBOqI0cymz378AJVyx34KAhMd9zh1kTYx3fOXcmfNxa0lmEUZxTDD2XYDIimMe56F9hqYNZVxZs6L8bhKGUe0zFL6tObOpD8/p1e6q/IXCGWjl8iNkujMUnn4PhaeiOHHuFQgV6TiioXLQa//PP+mlG/BJZLuciqEwrhUF1jZkxIcRnr7/Fww/e65RgM3K+k5uU0ZGFmKiIQKudvbWbPJ27tftZAriPLtRZ4nen1XXYK6raiI+OChVT+WTk39X7ZWTDtg83UYkaIMyoaqAjwA8d0lqWpbYeR+QjACY+dwajeuKtrc2GxPhe60O4xRntVBskop77Mn+iG/i95CIc+F1Mz66E76zOHv7BSrn4Eepn35wCbdAWohp3hqz4gDKjUWOe6FHM25YeXosaU/41sx57+RadtxAXbiTAp/NmQFJCKy3rBVzHF1r92PUTqvwnljX3Oc5vY5vQsil0PZa/74pNVCIlo7HPx86NrY07gUmGqwUzfQMIQmiyw9ovqKRhelG2kF5i6849gqWp9aHjEEtfduYetVTqog/Rj3mJtFXu+bq1mCby/z4WaGVaa/pyzWpVppS66SBr91SavRzhtphltJDwpWA/vAKRT3U6xUK1RvWcdM/HBIVtvf2BXPEuxMUHKehibKDd5/qeLD0KL8zUXsfFPA26q6HAckRnYow9KwFTDzlB0XT5loyXE9uunSPpo6IyJ+onBhBcFMQPrLd/UMKsJBvzLevFpPwhMUwL9PrRAb+1QeLe/Tu5QZrrTojYvjG2OFcLwD4njdbGs/mFarqu2uo09pj4tXAmapzO+GRd43VVKnscNr5tYuMFHKCXPz9vt3zS/DxDuN/YHXq2SFS4Q6oagdEVYCUYIPrTEYXCflbXAB4MM53oxFyQUU29cOFYrtAJW4cjD5rq2y5Z1wtwhkxY5xyEuX23Egjpz4VTYTN53qd4VYZ2ApPIn+c5yji0j15okIFoqxstfUMiAst2f4LLsCVXUjp6hi986b5/63TpgwjohUtE0JuQXFafWN5j+j7VGbryMwzEkZ7D4yO13GBFw8IRsTZozlmW44OPf0lNdnaDt0BoG7Rdu0/JMpSPP6wnmK9jyqzAKwXwDhiqTbQM5IXptaZhp2dNQWVZCl1bT4z0nItLIcNP5tMBTCoa0WI81FgcgROXJsBXB/g/xLaKBSCTTXH8dsLM2anuvZfuED+u5+XryY0sBW+aOk/4dPsPJ8Ns3+H9UMvAe5JMoa7ScVhnc2CcXwRIyK83LQxSXDmGTUFz3QDpyyDjHxgyPD80ECEWxiA7l3oG4+V+m0WEllRxcAxfnISMIcLyBzjFHH9I9lQhaWYE/PHyMb/qIzWXap7NBv7AGc4XjvAGivII/Ie4gPsT9o90RMeO5pKS/JGVdG6L4fM7k1JpVWuJEqi0I2mIdcqIERFjZ92yTCtB6FAT1y6RPnEvGPgILY/Wayn4/3BHf48ygnpCboobA0J8jyOK8UUD2tPrSgp5wDsF/1qjKEw1fSomBE0tDmYjrheRrVzG4fOoPaw7IO2ulKytf39+fQm12mv8MFOw5ngVxetALiKCn6Q1KL2ZEkkd/o4dAH65kMXY0tF24oQsixsOoIIT1E/iQsu4QY6/hMvD5OX2BLna394DKQ+CxQryp4N7MHC0CI84y4t8y7Uw6ngTWVQuQha8sAc/gD3C6gxsJgOaBC6KZqqx5ZPeLi12/fOcJE+rhKTnkwFjzWXcBz5NvA1lb32P3T3wSOwC6cGH4o39Y3a+PS66n0mhU4jlgzPNCdsZsvQHZyZyxqP5/H8mxR6ak6NOFJjUcf6VlzV3MgC1xKBLXAaXzdPCvBoI2ryXlENtguctoswXby3jJaXR94G+gdtjR8eezMjodwVMzrf7AY94lAQINAUIuEYrhUsjq25ktjdAxNgv2m6JBLp3096Nqk6gG4vkTL/mCi6LbnxWxWefGOhZYshqx3aIYifmQhmJqVQtm/ODe/PHlDVHj164jQ/jm65inAl+Z6gNLPiea0YcfWgC/US57mpdMYA9M4dl0VZTVFL3xkEV6r3qCJdGGBm0xRDIXcJwHvVzN9a1iChGBMuowkndFyBbFTpHZWQht5cfyfN4OB4raY73iWfQ6sTt7tuBqVccY1s9tcJSnfWIiyF0ohSmX94npQygBtaO9Z7J5u35sXJ7WNZTwbWNGhxQeSmI8ATEMhFtP5OzLd0SxJ3mjk23QU1+peKcGIDPQj8feSZxq7p6FKKh/E8+5LsLw4rF1LAKtP6d1vMoHxBDgddua3Yi7O0Jy0DRZFmjOhPvWFh3ur16SCOz2f+/lzno2W2KamcH0cHYzi2kfsQaoMNh2QdrORH81jMeF0HsyHQiR6xPCxUbIdbHFCvNDisL/sgDGlWwkFakO0+y9/FRxNXQ6h0wpoJRNOP+bsFwTO8IH0RHIJYJOQI8gDaERjuUj+VkNac2lCR0lytYUedgmH7yW+RO25QrNGdFkoidSPvFAGcS2MF+ovvcXpEvnHj3GxfJEtOzAeiEnMa+FGs6UZ1V4qVHCGZRgREqjiKpLsSOtg/hVyRdzeKpJex3N+d85RDQZEFHrC/fcvaIpKyzrXSgNCZ8kMQXJy2c3oa1CUkTCqBu6rAz8NBMslB7eY1nfzmudbJ/vDv2JlStkxU8Eeg7B2K9mne/gARKc5E79JNLeWXgh3W//aUYu3S5J/z/1Kg0G0Hvn7Lrkn8OTOo804vyUYj2qaHCD7XFfLHx/PnZaosJcyQtHR5AAIQdI7lbnp/2hv+DLZAWAoLCVsbDTySwDK1dQe+U9b9Qc71QarqKvprlHPA811SUp5HyMhD+M5j0QncwaO3e5aEHGuPpPR4vNt2P7DasQEAfU//cDLHbz/C4LpbNr1TVLtcTmOT7bgq3PpVCJqI+tgQdPwkNT1q3R7biYvqF8hFQegWULKMmSmrLvUToiVRKekOtMZ3U+vKogsILgN+egYgjPzP26dS7ZuresAN8kAtV6YV3MX3Hou1nJZNOMYaxZ/4ortY2o4aE/MaMQTX1lsuoy0F2lE0iJGEOIrFnQPg5ct11zIccOCoRP1r7puZN2UvfronlLaUWaVt9Y1OwbkAQ4VO+H7lx6XOtDHSrxCNpT0ONSBPJKpF95TuyWjgY7yQb+04DJPGi7zKFThU0cfTMlkN7WZESsBHhj8RVr6kmnXVw+iV+qXBl0bZsP70KVMAMBMkEexPdHhydPIbH9cUwKOSWBOjeGr9Tkd/CIEbGJYqXuXlfnry7m7Nd3Jqg+7gNm3o/w/QAmOvQ/XAWPFT/S9kid5Nntk3ACpsFeRdLspo6qsJIiDbWVSxLZwyPk32qhqhQ3J/WYNzyqtf6CBDk5srdS0iH35trfmvo24TQCm1lPn1CrwXZW1Ih5RnZVGwjgk7wbNpvoF30sZCJX8KOojhQh8imCeDIjwf87/r3InHRNTCGsBE3XrcvO5bP3iebupS3NV8ooTvox79ADifiPQWuZoN6rnREil455/2D05ETQtW/87kiQYaPfkPMVUKOuBPrR2CMmuhDJR76Hd7W49ISfgGetMel4xVwGQDUXHA1RNavNM/4