Low-cost imprecise robots achieve 80-90% success on six fine bimanual manipulation tasks using imitation learning with a new Action Chunking with Transformers algorithm trained on only 10 minutes of demonstrations.
Holo-dex: Teaching dexterity with immersive mixed reality
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DexTwist detects tripod pinches, estimates the intended screw axis and twist magnitude, then applies real-time joint refinement to track turning progress while stabilizing the robot's tripod geometry.
A robot generalizes one demonstration of placing a single brick to build walls of arbitrary length and layout by approximating motions as screw sequences and using ScLERP/RMRC for planning.
VR-DAgger is a VR-centered human-in-the-loop framework that applies MC dropout uncertainty to select and correct failure segments in diffusion policy rollouts, yielding up to 23 percentage point gains over behavioral cloning and 40% lower per-sample collection time on three dexterous tasks.
citing papers explorer
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Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Low-cost imprecise robots achieve 80-90% success on six fine bimanual manipulation tasks using imitation learning with a new Action Chunking with Transformers algorithm trained on only 10 minutes of demonstrations.
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DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation
DexTwist detects tripod pinches, estimates the intended screw axis and twist magnitude, then applies real-time joint refinement to track turning progress while stabilizing the robot's tripod geometry.
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Manipulation Planning for Construction Activities with Repetitive Tasks
A robot generalizes one demonstration of placing a single brick to build walls of arbitrary length and layout by approximating motions as screw sequences and using ScLERP/RMRC for planning.
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VR-DAgger: Immersive VR for Dexterous Data Collection and Uncertainty-Guided On-Policy Correction
VR-DAgger is a VR-centered human-in-the-loop framework that applies MC dropout uncertainty to select and correct failure segments in diffusion policy rollouts, yielding up to 23 percentage point gains over behavioral cloning and 40% lower per-sample collection time on three dexterous tasks.