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        <title>End-to-End Training of Deep Visuomotor Policies</title>
        <author>Sergey Levine</author>
        <author>Chelsea Finn</author>
        <author>Trevor Darrell</author>
        <author>Pieter Abbeel</author>
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          <title>End-to-End Training of Deep Visuomotor Policies</title>
          <date>2016</date>
          <publisher>JMLR / arXiv</publisher>
          
          <idno type="arXiv">1504.00702</idno>
          <idno type="record">levine2016-visuomotor</idno>
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          <note type="rights">arxiv-open</note>
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        <label>method</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t1" type="phenomenon" ana="#phenomenon" resp="#paper">
        <label>visuomotor policy learning</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t2" type="method" ana="#method" resp="#paper">
        <label>system-or-tool-building</label>
        <term>system-or-tool-building</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t3" type="mechanisms" ana="#mechanisms[0]" resp="#tagger">
        <label>mechanisms</label>
        <term>guided policy search: convert reinforcement-learning policy search into supervised learning of a deep network, with a trajectory-optimizer acting as the teacher</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t4" type="limitations" ana="#limitations[0]" resp="#tagger">
        <label>training requires positioning objects at consistent positions</label>
        <note>what this limits: the training procedure as described: it requires Quotation withheld</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The method does not generalise to dramatically different visual settings, especially under occluding distractors.</label>
        <note>what this limits: the generalisation claim: OUR CURRENT METHOD DOES NOT GENERALIZE TO DRAMATICALLY DIFFERENT SETTINGS, ESPECIALLY WHEN VISUAL DISTRACTO[rs occlude]</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The approach requires the full system state to be observable during training, which restricts the range of applicable tasks.</label>
        <note>what this limits: the range of tasks the method can address: THE REQUIREMENT TO OBSERVE THE FULL STATE DURING TRAINING LIMITS THE TASKS TO WHICH THE METHOD CAN BE APPLIED</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>Tasks with freely moving objects would need heavier instrumentation such as motion capture.</label>
        <note>what this limits: any extension to freely moving objects: those TASKS ... REQUIRE MORE EXTENSIVE INSTRUMENTATION, SUCH AS MOTION CAPTURE</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The spatial feature-point representation makes simplifying assumptions about feature presence and uniqueness.</label>
        <note>what this limits: every result produced through the visual encoder: THE FEATURE POINT REPRESENTATION ... ASSUMES THAT THE LEARNED FEATURES ARE PRESENT AT ALL TIMES, AND ONLY ONE INSTANCE OF EACH FEATURE IS EVER PRESENT IN THE IMAGE</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t9" type="limitations" ana="#limitations[5]" resp="#tagger" cert="1.0">
        <label>The method needs the full state observable during training, and the authors say exactly when that is minor and when it is not.</label>
        <note>what this limits: applying this method to a new task: the full state must be observable during training, which the authors call minor when objects can be positioned consistently and not minor for tasks manipulating freely moving objects</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The architecture comparison is not run on the target task but on a pretraining task, which the authors describe as a reasonable proxy.</label>
        <note>what this limits: the architecture comparison in this paper: it is measured on the pose-estimation pretraining task, described by the authors as a reasonable proxy for the visuomotor challenges</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t11" type="limitations" ana="#limitations[7]" resp="#tagger" cert="0.95">
        <label>The training-time observability the method depends on is bought with a physical constraint on the world, stated plainly: providing the full state &#x27;simply requires positioning objects in one of several known positions for each trial during training&#x27;. The assumption is not about the algorithm but about how the laboratory is arranged.</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t12" type="transfers" ana="#transfers[0]" resp="#tagger">
        <label>deep convolutional networks trained end-to-end from perception to control for robotic manipulation</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t13" type="controls" ana="#controls[0]" resp="#tagger" cert="0.9">
        <label>two baselines that pre-train the vision layers for pose prediction instead of training end-to-end, one with the SAME architecture as the full policy</label>
        <note>eliminates: that the improvement comes from the network architecture rather than from training the vision and control layers jointly end-to-end · outcome: held</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>visuomotor policy</label>
        <term>visuomotor policy</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>guided policy search</label>
        <term>guided policy search</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>pose estimation</label>
        <term>pose estimation</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>supervised learning</label>
        <term>supervised learning</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>architecture quality - MEASURED ON A PROXY: accuracy on the pose-estimation pretraining task, chosen to isolate the architectures from other confounding factors</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>failure at insertion - defined by a threshold taken from the object itself: since the peg is 0.5 units long, a larger distance means the controller cannot insert</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>observable - split by phase, and the split is the method&#x27;s central assumption: the full system state is observable AT TRAINING TIME BUT NOT AT TEST TIME, which is what lets a trajectory-centric RL algorithm supply supervision the deployed policy never gets</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>builds-on → convolutional neural networks (deep learning)</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>builds-on → reinforcement learning</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>builds-on → Bregman ADMM (BADMM) (Wang and Banerjee, 2014)</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t24" type="claim_relations" ana="#claim_relations[3]" resp="#tagger">
        <label>extends → Levine and Abbeel, 2014; Levine et al., 2015 (the authors&#x27; own conference papers)</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t25" type="claim_relations" ana="#claim_relations[4]" resp="#tagger">
        <label>extends → Mordatch and Todorov (2014)</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>guided policy search —operationalised-as→ supervised learning</label>
        <term>guided policy search</term>
        <term>supervised learning</term>
        <note>strength of the relation: 0.9 · note: THE METHOD&#x27;S WHOLE MOVE IS A CONVERSION BETWEEN PROBLEM CLASSES, stated as such: &#x27;…search TRANSFORMS policy search INTO A SUPERVISED LEARNING…&#x27; The hard problem is not solved; it is turned into an easier one whose labels another algorithm supplies.
Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says guided policy search TRANSFORMS policy search into a supervised learning problem; operationalised-as is loose.</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t27" type="content_tags" ana="#content_tags.quantities[0]" resp="#tagger">
        <label>92,000 — size of the CNN policy network</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>7 — depth of the policy CNN</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t29" type="content_tags" ana="#content_tags.quantities[2]" resp="#tagger">
        <label>tens of minutes — real-world data needed to train a policy</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>2 — prior papers this article extends</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>end-to-end training improves consistency and generalization</label>
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        <label>the policy maps raw camera images directly to motor torques</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t33" type="content_tags" ana="#content_tags.findings[3]" resp="#tagger">
        <label>full system state is available at training but not at test time</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>raw image observations</label>
        <term>raw image observations</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>motor torques</label>
        <term>motor torques</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>cost function</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>full state</label>
        <term>full state</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>guided policy search</label>
        <term>guided policy search</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>visuomotor policies</label>
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        <label>policy search</label>
        <term>policy search</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>Bregman ADMM (BADMM)</label>
        <term>Bregman ADMM (BADMM)</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>regression</label>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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