Human-as-Humanoid converts ego-exo human videos into executable 60-DoF humanoid actions through embodiment alignment and retargeting, enabling zero-shot real-robot policy deployment without target-task teleoperation data.
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Dex- cap: Scalable and portable mocap data collection system for dexterous manipulation
Canonical reference. 88% of citing Pith papers cite this work as background.
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representative citing papers
AutoDex automates the full perception-execution-labeling-reset loop for real-world dexterous grasping data collection, delivering 4.8x throughput over teleoperation and 76% success for retrieved grasps versus 34% from simulation-only data.
GLAM learns a shared latent action space grounded in consistent future observation prediction across heterogeneous data sources to train improved behavioral cloning policies for robot manipulation tasks.
A wearable interface with a shared dexterous hand module enables retargeting-free teleoperation and matched data collection, yielding policies with 88.75% average success across eight real-robot tasks that generalize and transfer across embodiments.
MonoDuo generates synthetic bimanual demonstrations from single-arm teleoperation plus human collaboration to train policies achieving up to 70% zero-shot success on five manipulation tasks, with 65-70% gains from 25-shot finetuning.
DexJoCo is a benchmark and toolkit with 11 functionally grounded tasks, 1.1K trajectories, and empirical benchmarks for task-oriented dexterous manipulation on MuJoCo.
HandITL enables seamless human intervention in VLA policies for bimanual dexterous manipulation, cutting jitter by 99.8% and improving refined policies by 19% over standard teleoperation.
FingerViP equips each finger with a miniature camera and trains a multi-view diffusion policy that achieves 80.8% success on real-world dexterous tasks previously limited by wrist-camera occlusion.
DEX-Mouse is a portable, calibration-free teleoperation interface under $150 with kinesthetic force feedback that supports mounting the robot hand on the operator's forearm for aligned data collection, achieving 86.67% task completion and lower perceived workload than separated setups.
ActiveGlasses learns robot manipulation from ego-centric human demos captured with active vision via smart glasses, achieving zero-shot transfer using object-centric point-cloud policies.
TeleGate achieves high-precision real-time whole-body teleoperation of humanoid robots by dynamically gating between expert policies and using a VAE motion prior to infer future intent from history, outperforming distillation baselines on dynamic motions with only 2.5 hours of mocap data.
Uni-Hand forecasts 2D/3D hand waypoints, head motion, and contact states in egocentric views using vision-language fusion and dual-branch diffusion, with new benchmarks for downstream robotics and action tasks.
A hybrid event-driven switching system pairs VLA models with lightweight dexterous policies on a compliant anthropomorphic hand to perform language-conditioned multi-finger tasks with cross-embodiment modularity.
DP3 uses compact 3D representations from sparse point clouds inside diffusion policies to learn generalizable visuomotor skills from few demonstrations, reporting 24% gains in simulation and 85% success on real robots.
CoDex combines VLMs, constrained optimization, and RL to autonomously discover grasp-move-actuate policies for functional manipulation of unseen objects with internal mechanisms.
DexAC-WM improves FID, FVD, and PCK in high-DoF action-conditioned video prediction via structured action modeling and semantic grounding on EgoDex and EgoVerse.
Play2Perfect uses task-agnostic RL play pretraining on diverse objects to build reusable manipulation priors, then fine-tunes for assembly, yielding 33x sample efficiency gains and 60% success on 0.5mm-clearance insertions in sim-to-real transfer.
ZeroDex grounds VLM outputs into 3D keypoints via multi-view triangulation and ray voting to enable zero-shot long-horizon dexterous manipulation with closed-loop replanning.
TopoRetarget uses a sparse interaction graph and distance-weighted Laplacian deformation optimization with kinematic and penetration constraints to retarget human demonstrations to dexterous hands while preserving task-relevant contacts.
Presents arm-worn AetheRock hardware for multi-modal data collection and ForceVT learning method to improve tactile inference robustness despite sensor variations.
DexPIE improves dexterous manipulation success rates by 37% over demo policies via real-world experience collection with adapted intervention, multi-stage DAgger, asynchronous relative-action inference, and optimality conditioning.
FlexiTac is a scalable piezoresistive tactile sensing system with flexible FPC-Velostat-FPC pads and a 100 Hz multi-channel readout board that mounts on rigid or soft grippers and supports visuo-tactile learning.
EaDex combines single-camera RGB-D capture, MANO retargeting, and dynamic demonstration annealing to achieve 55.3% relative improvement over baseline on nine cross-embodiment dexterous object-opening tasks across three hands.
GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalization from sparse data.
citing papers explorer
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Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments
Human-as-Humanoid converts ego-exo human videos into executable 60-DoF humanoid actions through embodiment alignment and retargeting, enabling zero-shot real-robot policy deployment without target-task teleoperation data.
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AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection
AutoDex automates the full perception-execution-labeling-reset loop for real-world dexterous grasping data collection, delivering 4.8x throughput over teleoperation and 76% success for retrieved grasps versus 34% from simulation-only data.
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Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models
GLAM learns a shared latent action space grounded in consistent future observation prediction across heterogeneous data sources to train improved behavioral cloning policies for robot manipulation tasks.
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RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning
A wearable interface with a shared dexterous hand module enables retargeting-free teleoperation and matched data collection, yielding policies with 88.75% average success across eight real-robot tasks that generalize and transfer across embodiments.
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MonoDuo: Using One Robot Arm to Learn Bimanual Policies
MonoDuo generates synthetic bimanual demonstrations from single-arm teleoperation plus human collaboration to train policies achieving up to 70% zero-shot success on five manipulation tasks, with 65-70% gains from 25-shot finetuning.
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DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
DexJoCo is a benchmark and toolkit with 11 functionally grounded tasks, 1.1K trajectories, and empirical benchmarks for task-oriented dexterous manipulation on MuJoCo.
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Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention
HandITL enables seamless human intervention in VLA policies for bimanual dexterous manipulation, cutting jitter by 99.8% and improving refined policies by 19% over standard teleoperation.
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FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception
FingerViP equips each finger with a miniature camera and trains a multi-view diffusion policy that achieves 80.8% success on real-world dexterous tasks previously limited by wrist-camera occlusion.
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DEX-Mouse: A Low-cost Portable and Universal Interface with Force Feedback for Data Collection of Dexterous Robotic Hands
DEX-Mouse is a portable, calibration-free teleoperation interface under $150 with kinesthetic force feedback that supports mounting the robot hand on the operator's forearm for aligned data collection, achieving 86.67% task completion and lower perceived workload than separated setups.
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ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration
ActiveGlasses learns robot manipulation from ego-centric human demos captured with active vision via smart glasses, achieving zero-shot transfer using object-centric point-cloud policies.
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TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior
TeleGate achieves high-precision real-time whole-body teleoperation of humanoid robots by dynamically gating between expert policies and using a VAE motion prior to infer future intent from history, outperforming distillation baselines on dynamic motions with only 2.5 hours of mocap data.
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Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views
Uni-Hand forecasts 2D/3D hand waypoints, head motion, and contact states in egocentric views using vision-language fusion and dual-branch diffusion, with new benchmarks for downstream robotics and action tasks.
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Language Conditioned Multi-Finger Dexterous Manipulation Enabled by Physical Compliance and Switching of Controllers
A hybrid event-driven switching system pairs VLA models with lightweight dexterous policies on a compliant anthropomorphic hand to perform language-conditioned multi-finger tasks with cross-embodiment modularity.
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3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
DP3 uses compact 3D representations from sparse point clouds inside diffusion policies to learn generalizable visuomotor skills from few demonstrations, reporting 24% gains in simulation and 85% success on real robots.
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CoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations
CoDex combines VLMs, constrained optimization, and RL to autonomously discover grasp-move-actuate policies for functional manipulation of unseen objects with internal mechanisms.
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Not All Actions Are Equal: Rethinking Conditioning for Dexterous World Model
DexAC-WM improves FID, FVD, and PCK in high-DoF action-conditioned video prediction via structured action modeling and semantic grounding on EgoDex and EgoVerse.
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Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Play2Perfect uses task-agnostic RL play pretraining on diverse objects to build reusable manipulation priors, then fine-tunes for assembly, yielding 33x sample efficiency gains and 60% success on 0.5mm-clearance insertions in sim-to-real transfer.
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ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning
ZeroDex grounds VLM outputs into 3D keypoints via multi-view triangulation and ray voting to enable zero-shot long-horizon dexterous manipulation with closed-loop replanning.
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TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
TopoRetarget uses a sparse interaction graph and distance-weighted Laplacian deformation optimization with kinematic and penetration constraints to retarget human demonstrations to dexterous hands while preserving task-relevant contacts.
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AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
Presents arm-worn AetheRock hardware for multi-modal data collection and ForceVT learning method to improve tactile inference robustness despite sensor variations.
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DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
DexPIE improves dexterous manipulation success rates by 37% over demo policies via real-world experience collection with adapted intervention, multi-stage DAgger, asynchronous relative-action inference, and optimality conditioning.
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FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems
FlexiTac is a scalable piezoresistive tactile sensing system with flexible FPC-Velostat-FPC pads and a 100 Hz multi-channel readout board that mounts on rigid or soft grippers and supports visuo-tactile learning.
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EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations
EaDex combines single-camera RGB-D capture, MANO retargeting, and dynamic demonstration annealing to achieve 55.3% relative improvement over baseline on nine cross-embodiment dexterous object-opening tasks across three hands.
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General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling
GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalization from sparse data.
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Towards Robotic Dexterous Hand Intelligence: A Survey
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.
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World Action Models: The Next Frontier in Embodied AI
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
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StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
StereoPolicy fuses left-right image features via cross-attention to deliver consistent gains over RGB, RGB-D, point cloud, and multi-view baselines in simulation and real-robot manipulation tasks.
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Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach
A position paper proposes decomposing affective robotic touch into multiple specialized deep learning models for better social human-robot interaction.
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Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines
A survey of VLA robotics research identifies data infrastructure as the primary bottleneck and distills four open challenges in representation alignment, multimodal supervision, reasoning assessment, and scalable data generation.