HumDex: Humanoid Dexterous Manipulation Made Easy

Humans routinely wield tools, swap grasps, and reposition objects within a single hand, seamlessly orchestrating contact transitions that span translation, reorientation, and finger gaiting. Endowing robot dexterous hands with this level of in-hand dexterity through teleoperation requires precise control of object motion via dynamic hand-object contact, yet current teleoperation systems remain far from this capability. To bridge this gap, we take a major step towards human-level dexterous teleoperation by introducing TeleDexter, a hand-object co-tracking controller that maps operator intent into learned, low-level contact execution. The controller is trained on consecutive co-tracking subgoals derived from human reference motions, utilizing a hybrid reward that couples sparse subgoal objectives with dense tracking rewards to enable learning across diverse interaction modalities rather than frame-wise trajectory imitation. The entire pipeline requires only single-stage RL and, with random action masking and domain randomization, transfers zero-shot to the real robot. We evaluate TeleDexter on seven challenging dexterous teleoperation tasks spanning object reorientation and long-horizon tool use across two dexterous hands, achieving a 75% average success rate where all baselines consistently fail. Furthermore, the collected demonstrations successfully train autonomous policies via behavioral cloning, marking a concrete step towards human-level dexterous teleoperation.

High-fidelity teleoperation of dexterous robotic hands is essential for bringing robots into unstructured domestic environments. However, existing teleoperation systems often face a trade-off between performance and portability: vision-based capture systems are constrained by costs and line-of-sight requirements, while mechanical exoskeletons are bulky and physically restrictive. In this paper, we present DexEMG, a lightweight and cost-effective teleoperation system leveraging surface electromyography (sEMG) to bridge the gap between human intent and robotic execution. We first collect a synchronized dataset of sEMG signals and hand poses via a MoCap glove to train EMG2Pose, a neural network capable of continuously predicting hand kinematics directly from muscle activity. To ensure seamless control, we develop a robust hand retargeting algorithm that maps the predicted poses onto a multi-fingered dexterous hand in real-time. Experimental results demonstrate that DexEMG achieves high precision in diverse teleoperation tasks. Notably, our system exhibits strong generalization capabilities across novel objects and complex environments without the need for intensive individual-specific recalibration. This work offers a scalable and intuitive interface for both general-purpose robotic manipulation and assistive technologies.

Humanoid teleoperation for demonstration collection requires coordinated whole-body motion, continuous dexterous hand control, and viewpoint control. Existing systems either simplify hand commands or depend on dedicated wearable sensors for fine-grained hand motion. We introduce Teleopit, a full-embodiment teleoperation system that maps body, hand, and head signals from VR to a humanoid body, configurable dexterous hands, and a 2-DoF active vision module. A history encoder and failure-aware rewind sampling improve the motion tracker on both motion-capture and live VR references. An optimization-based hand retargeter combines normalized finger directions, fingertip closure, and thumb-frame alignment to map human hand motion to different dexterous hands without tuning hand-specific objective or solver hyperparameters. Component experiments evaluate tracking success rate and retargeting behavior, while real-robot teleoperation demonstrates coordinated locomotion, manipulation, and viewpoint control. ACT and GR00T N1.7 policies trained on 96 successful demonstrations collected with Teleopit achieve task success rates of 90.0% and 95.0%, respectively, when deployed on the humanoid. The project page is available at https://botrunner64.github.io/teleopit-page.