Foresight detects failures in long-horizon robotic manipulation using latents from action-conditioned world models trained only on task-level labels and calibrated via functional conformal prediction.
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MoMo conditions contrastive representations and prediction operators on user preferences via FiLM and low-rank modulation to enable continuous modulation of plan safety while preserving inference efficiency.
Vision-guided dual-arm robotic pipeline achieves 8/10 success disassembling 21-cell 18650 packs from arbitrary poses with 2.4 mm localization error and 6-minute cycle time using RGB-D sensing and general grippers.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
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Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents
Foresight detects failures in long-horizon robotic manipulation using latents from action-conditioned world models trained only on task-level labels and calibrated via functional conformal prediction.
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MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning
MoMo conditions contrastive representations and prediction operators on user preferences via FiLM and low-rank modulation to enable continuous modulation of plan safety while preserving inference efficiency.
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Vision-Guided Dual-Arm Humanoid Robotic Disassembly of End-of-Life 18650 Lithium-ion Battery Packs
Vision-guided dual-arm robotic pipeline achieves 8/10 success disassembling 21-cell 18650 packs from arbitrary poses with 2.4 mm localization error and 6-minute cycle time using RGB-D sensing and general grippers.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.