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Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning
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Teleoperation is a crucial tool for collecting human demonstrations, but controlling robots with bimanual dexterous hands remains a challenge. Existing teleoperation systems struggle to handle the complexity of coordinating two hands for intricate manipulations. We introduce Bunny-VisionPro, a real-time bimanual dexterous teleoperation system that leverages a VR headset. Unlike previous vision-based teleoperation systems, we design novel low-cost devices to provide haptic feedback to the operator, enhancing immersion. Our system prioritizes safety by incorporating collision and singularity avoidance while maintaining real-time performance through innovative designs. Bunny-VisionPro outperforms prior systems on a standard task suite, achieving higher success rates and reduced task completion times. Moreover, the high-quality teleoperation demonstrations improve downstream imitation learning performance, leading to better generalizability. Notably, Bunny-VisionPro enables imitation learning with challenging multi-stage, long-horizon dexterous manipulation tasks, which have rarely been addressed in previous work. Our system's ability to handle bimanual manipulations while prioritizing safety and real-time performance makes it a powerful tool for advancing dexterous manipulation and imitation learning.
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Cited by 35 Pith papers
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DexterityGen: Foundation Controller for Unprecedented Dexterity
A pretrained generative controller can convert coarse, unsafe teleoperation finger commands into stable, dexterous robot hand motions, enabling object reorientation and basic tool use.
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HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing
HandEdit is a benchmark and 200M-instance dataset that converts egocentric human hand manipulation frames into 26 distinct URDF-specified robot hand and hand-arm embodiments.
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Smooth Operator: A Real-Time Sampling-Based Algorithm for Kinematic Hand Retargeting
Sampling-Based Retargeter (SBR) delivers lower-jitter real-time kinematic hand retargeting and higher task success with less operator fatigue than gradient-based baselines in an 18-person study.
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Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration
Dexplore learns dexterous robotic hand control from human MoCap demonstrations by treating them as soft, adaptively shrinking spatial references, then distills the policy into a vision-based controller.
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DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
DEXOP implements perioperation with a passive exoskeleton linked to a sensorized robot hand, and DEXOP-collected demonstrations train robot policies more efficiently per unit time than teleoperation.
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ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training
ManiFlow trains a flow-matching policy with a continuous-time consistency objective and an adaptive cross-attention transformer, enabling dexterous manipulation with 1-2 inference steps and substantially higher succes...
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LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
LodeStar combines automatic skill segmentation with simulation-based reinforcement learning augmentation and a learned routing transformer to let a robotic hand complete long-horizon dexterous tasks from a few human demos.
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BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots
BEAVR provides an open-source, low-cost VR teleoperation pipeline for multiple robot embodiments, with LeRobot-format data recording and compatibility with ACT, Diffusion Policy, and SmolVLA.
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PAPRLE (Plug-And-Play Robotic Limb Environment): A Modular Ecosystem for Robotic Limbs
PAPRLE is a modular teleoperation ecosystem that pairs diverse input devices with arbitrary robot limb configurations and adds force feedback even when leader and follower morphologies differ.
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RwoR: Generating Robot Demonstrations from Human Hand Collection for Policy Learning without Robot
A generative model and wrist camera turn human hand videos into robot gripper demonstrations that train manipulation policies at success rates close to those trained on real gripper data.
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TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
A type-guided teleoperation system that selects predefined dexterous hand poses with a language model outperforms retargeting-based teleoperation on nine real-world tasks and improves imitation learning success.
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Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining
Pretraining a modular transformer policy on human demonstrations then finetuning on a small robot dataset improves success on six real quadruped manipulation tasks, including out-of-distribution objects.
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ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
A simulation-trained teacher-student policy achieves zero-shot sim-to-real closed-loop target-oriented dexterous grasping in cluttered scenes, with 83.9 percent real-world success.
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DemoSpeedup: Accelerating Visuomotor Policies via Entropy-Guided Demonstration Acceleration
DemoSpeedup accelerates visuomotor policies by downsampling high-entropy segments of demonstrations, achieving roughly 2x faster execution with maintained or improved success rates.
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Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction
CogRobot uses optical flow as an intermediate variable to fine-tune a text-to-video model for predicting bimanual robot trajectories, then maps those predictions to actions with a goal-conditioned diffusion policy.
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SuFIA-BC: Generating High Quality Demonstration Data for Visuomotor Policy Learning in Surgical Subtasks
SuFIA-BC introduces a photorealistic surgical digital-twin benchmark and shows that current behavior cloning policies, whether RGB or point-cloud based, struggle on contact-rich surgical subtasks.
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RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins
Pre-training on RoboTwin's generative digital twins and fine-tuning on 20 real demonstrations raises dual-arm task success from about 20% to 62% and single-arm success from about 1% to 72%.
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Global-Local Interface for On-Demand Teleoperation
A Global-Local teleoperation interface that separates coarse positioning from fine manipulation lets operators complete precise tasks faster and with higher success than using either mode alone.
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DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove
DOGlove is a low-cost, open-source haptic glove for dexterous teleoperation that improves performance on contact-rich tasks and supplies demonstrations for imitation learning.
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You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations
From one human hand demonstration, YOTO generates hundreds of robot demonstrations and trains a bimanual diffusion policy that outperforms ACT, DP, DP3, and EquiBot on five real-world tasks.
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Learning to Transfer Human Hand Skills for Robot Manipulations
A retargeting model learns a joint manifold of human hand, robot hand, and object trajectories, trained on synthetic pseudo-ground-truth triplets, and transfers human mocap demonstrations to a real robot hand.
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ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data Acquisition
Live augmented-reality feedback from a virtual robot raises the hardware replay success of barehanded human demonstrations from 1.3% to 71.1%, enabling robot-free data collection for imitation learning.
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AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation
AnyBimanual transfers pretrained unimanual robot policies to bimanual manipulation via a skill manager and a visual aligner, achieving 32.00% average success on 12 RLBench2 tasks.
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WildLMa: Long Horizon Loco-Manipulation in the Wild
WildLMa combines VR teleoperation with whole-body control, CLIP-based language-conditioned imitation learning, and an LLM planner to give a quadruped robot reusable manipulation skills that generalize to unseen object...
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AsymDex: Asymmetry and Relative Coordinates for RL-based Bimanual Dexterity
AsymDex trains two multi-fingered robot hands for bimanual tasks by assigning asymmetric roles and using relative coordinates, beating baselines in success and sample efficiency.
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MEVION: Low-Cost Open-Source Data Collection System for Powerful and High-Speed Dual-Arm Manipulation
MEVION is an open-source dual-arm teleoperation platform with 60 Nm joint torque, built from e-commerce parts for about $14,000 per four-arm system, enabling heavier, faster manipulation data collection.
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ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation
A co-training framework that maps retargeted human hand trajectories to robot demonstrations with dynamic time warping and MixUp interpolation improves robot manipulation success rates and smoothness across four embodiments.
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KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation
KineDex shows that kinesthetic teaching with inpainting and force-informed actions trains tactile-aware visuomotor policies that outperform position-only control on nine dexterous manipulation tasks.
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TelePreview: A User-Friendly Teleoperation System with Virtual Arm Assistance for Enhanced Effectiveness
TelePreview adds a physically aligned augmented-reality preview and a preview/execute foot-pedal switch to low-cost glove-and-IMU teleoperation, and reports higher success rates and shorter execution times in a five-t...
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FunGrasp: Functional Grasping for Diverse Dexterous Hands
Given one RGBD image of a human grasping an object, FunGrasp retargets the grasp to several robot hands and achieves functional real-world grasping of unseen objects.
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Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions
Behavior-cloning pretraining from AR demonstrations plus a contrastive projection-head loss in SAC improves dexterous grasping success by ~8 points and cuts training time ~4x in simulation.
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An Immersive Virtual Reality Bimanual Telerobotic System With Haptic Feedback
Finger force feedback in a bimanual VR teleoperation system improved blind grasping, in-hand manipulation, and fragile-object transport in small user experiments.
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Modality-Driven Design for Multi-Step Dexterous Manipulation: Insights from Neuroscience
A neuroscience-inspired, modality-driven pipeline with classical control, a vision-language-action model, and force-feedback RL performs pick-and-rotate on a real robot, but only 5 of 35 trials complete all steps.
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Data Pyramid for Embodied Manipulation: A Survey
Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.
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UTTG_ A Universal Teleoperation Approach via Online Trajectory Generation
UTTG is a teleoperation framework that reads robot URDF files and applies smoothing-spline interpolation to convert low-rate human commands into high-frequency joint trajectories across three robot platforms.
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