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@@ -161,12 +160,8 @@ export default function FirstPost() {
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/>
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</div>
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{/* <p style={{marginTop: '20px'}}>
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We validate DreamZero on two robot embodiments: <em>AgiBot G1</em> (mobile bimanual manipulator) and <em>Franka</em> (single-arm robot). For AgiBot, we pretrain on ~500 hours of diverse "on-the-job" (data collected maximizing utility) teleoperation data collected across 22 real-world environments—homes, restaurants, supermarkets, coffee shops, and offices. For Franka, we train on <em>DROID</em>, one of the most heterogeneous publicly available robotic datasets. Below, we demonstrate DreamZero's capabilities across 5 evaluations: <em>#1 AgiBot pretrain seen & unseen tasks</em>, <em>#2 DROID pretrain seen tasks, unseen objects, and unseen verbs</em>, <em>#3 AgiBot post-train out-of-distribution (3 tasks)</em>, <em>#4 Emergent Capabilities from WAMs (tool use, human-robot-interaction, collision avoidance, and visual reasoning)</em>, and <em>#5 Different variations and ablations of DreamZero for real-time inference.</em>.
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</p> */}
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<pstyle={{marginTop: '20px'}}>
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We validate DreamZero on two robot embodiments: <em>AgiBot G1</em> (mobile bimanual manipulator) and <em>Franka</em> (single-arm robot). For AgiBot, we pretrain on ~500 hours of diverse "on-the-job" (data collected maximizing utility) teleoperation data collected across 22 real-world environments—homes, restaurants, supermarkets, coffee shops, and offices. For Franka, we train on <em>DROID</em>, one of the most heterogeneous publicly available robotic datasets. Below, we demonstrate DreamZero's capabilities across 5 evaluations: <em>#1 AgiBot pretrain seen &unseentasks</em>, <em>#2 DROID pretrain seen tasks, unseen objects, and unseen verbs</em>, <em>#3 AgiBot post-train out-of-distribution (3 tasks)</em>, <em>#4 Interactive Prompting</em>, and <em>#5 Different variations and ablations of DreamZero for real-time inference.</em>.
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We validate DreamZero on two robot embodiments: <em>AgiBot G1</em> (mobile bimanual manipulator) and <em>Franka</em> (single-arm robot). For AgiBot, we pretrain on ~500 hours of diverse "on-the-job" (data collected maximizing utility) teleoperation data collected across 22 real-world environments—homes, restaurants, supermarkets, coffee shops, and offices. For Franka, we train on <em>DROID</em>, one of the most heterogeneous publicly available robotic datasets. Below, we demonstrate DreamZero's capabilities across 5 evaluations: <em>#1 AgiBot pretrain seen &unseentasks</em>, <em>#2 DROID pretrain seen tasks and unseen verbs</em>, <em>#3 AgiBot post-train out-of-distribution (3 tasks)</em>, <em>#4 Interactive Prompting</em>, and <em>#5 Different variations and ablations of DreamZero for real-time inference.</em>.
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</p>
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{/* Behavior Generalization Videos */}
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{/* Environment Generalization */}
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<divclassName={styles.blogContent}>
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<h3>#2. DROID pretrain seen tasks, unseen objects, and unseen verbs</h3>
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<h3>#2. DROID pretrain seen tasks and unseen verbs</h3>
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<p>Select an environment task to see the corresponding neural trajectory and real-robot execution videos:</p>
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<ButtonSelector
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options={[
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{id: 'droid_1',label: 'Seen Tasks'},
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{id: 'droid_2',label: 'Unseen Objects'},
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{id: 'droid_3',label: 'Unseen Verbs'},
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{id: 'droid_2',label: 'Unseen Verbs'},
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]}
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selectedId={selectedDroid}
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onSelect={(option)=>setSelectedDroid(option.id)}
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<h3>#4. Interactive Prompting</h3>
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{/* <p>Leveraging the priors of WAMs, we observe that DreamZero some <em>emergent</em> capabilities and is able to accomplish tasks that previously required task-specific methods.</p> */}
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{/* <ButtonSelector
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options={[
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{ id: 'push_elevator', label: 'Push Elevator Button' },
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