REVIEW 28 cited by
EgoMimic: Scaling Imitation Learning via Egocentric Video
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
EgoMimic: Scaling Imitation Learning via Egocentric Video
read the original abstract
The scale and diversity of demonstration data required for imitation learning is a significant challenge. We present EgoMimic, a full-stack framework which scales manipulation via human embodiment data, specifically egocentric human videos paired with 3D hand tracking. EgoMimic achieves this through: (1) a system to capture human embodiment data using the ergonomic Project Aria glasses, (2) a low-cost bimanual manipulator that minimizes the kinematic gap to human data, (3) cross-domain data alignment techniques, and (4) an imitation learning architecture that co-trains on human and robot data. Compared to prior works that only extract high-level intent from human videos, our approach treats human and robot data equally as embodied demonstration data and learns a unified policy from both data sources. EgoMimic achieves significant improvement on a diverse set of long-horizon, single-arm and bimanual manipulation tasks over state-of-the-art imitation learning methods and enables generalization to entirely new scenes. Finally, we show a favorable scaling trend for EgoMimic, where adding 1 hour of additional hand data is significantly more valuable than 1 hour of additional robot data. Videos and additional information can be found at https://egomimic.github.io/
Forward citations
Cited by 28 Pith papers
-
HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining
Processed egocentric human video outperforms teleoperated real-robot trajectories as pretraining data for embodied foundation models, delivering 24% lower validation loss and 52.5-90% higher task success rates under m...
-
Human Universal Grasping
HUG trains a flow-matching model on a new 1M-frame egocentric human grasp dataset to generate retargetable grasps from single RGB-D images, beating baselines by 23-34% on a new 90-object benchmark.
-
EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
EgoEngine transforms egocentric human videos into high-fidelity robot data enabling zero-shot visuomotor dexterous policy learning without real-robot demonstrations.
-
SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation
SABER provides 44.8K multi-representation action samples from unscripted retail environments that raise a VLA model's mean success rate on ten manipulation tasks from 13.4% to 29.3%.
-
EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data
World Action Model co-training with DINO or 3D-flow targets scales human-to-robot transfer on bimanual tasks far better than behavior cloning, while pixel prediction transfers weakly.
-
WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
A frozen world-action model can be steered to new tasks by adapting a lightweight memory from unlabeled human video via test-time training.
-
T-Rex: Tactile-Reactive Dexterous Manipulation
T-Rex introduces a large tactile dataset and MoT architecture that achieves over 30% higher success rates than baselines on 12 tasks requiring force control and deformable object handling.
-
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-...
-
HumanNet: Scaling Human-centric Video Learning to One Million Hours
HumanNet is a 1M-hour human-centric video dataset with interaction annotations that enables better vision-language-action model performance than equivalent robot data in a controlled test.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere releases an open mobile app, processing pipeline, and 200-hour dataset for long-horizon egocentric data collection on commodity hardware to support vision-language-action model training.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere supplies an open mobile app, 200-hour long-form egocentric dataset, and processing pipeline that enables collection of persistent-state egocentric trajectories on commodity hardware for VLA and foun...
-
Bridging the Embodiment Gap: Disentangled Cross-Embodiment Video Editing
A dual-contrastive disentanglement method factorizes videos into independent task and embodiment latents, then uses a parameter-efficient adapter on a frozen video diffusion model to synthesize robot executions from s...
-
RoSHI: A Versatile Robot-oriented Suit for Human Data In-the-Wild
RoSHI is a hybrid wearable that combines sparse IMUs and egocentric SLAM to capture accurate full-body 3D pose and shape data in natural environments for robot learning.
-
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.
-
EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
EgoVLA pretrains VLA models on egocentric human videos, retargets predicted actions to robots via IK, and fine-tunes on few robot demos to improve bimanual manipulation performance on a new simulation benchmark.
-
Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations
RIGVid shows that filtered AI-generated videos can serve as effective supervision for complex robotic manipulation tasks without any real demonstrations.
-
Native Video-Action Pretraining for Generalizable Robot Control
A video-action foundation model pretrained natively for embodiment achieves few-shot generalization and 225 Hz real-time closed-loop robot control.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
An open framework with a free smartphone app, STERA pipeline, and 200-hour dataset enables hour-plus egocentric data collection on commodity hardware and demonstrates utility by lowering VLA action-prediction error af...
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere provides an open infrastructure and 200-hour dataset for collecting long-horizon egocentric trajectories on commodity phones to support VLA model training.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere releases an open-source smartphone app, 200-hour egocentric dataset with persistent tracking, and pipeline to enable long-horizon data collection for VLA and foundation model research on commodity hardware.
-
VLBiMan: Vision-Language Anchored One-Shot Demonstration Enables Generalizable Bimanual Robotic Manipulation
VLBiMan framework enables generalizable bimanual manipulation from single human demonstrations via vision-language anchored task decomposition and adaptation without retraining.
-
Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
-
GR-3 Technical Report
GR-3 is a VLA model that generalizes to novel objects, environments, and abstract instructions, outperforms the π0 baseline, and integrates with the new ByteMini bi-manual mobile robot.
-
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
Multi-task pretraining of diffusion policies on diverse robot data produces more successful, robust, and data-efficient policies for dexterous manipulation than single-task baselines, with performance scaling with pre...
-
RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning
RDGen uses sim-to-real RL policies to generate smoother robot demonstrations that improve downstream VLA performance over human-collected data on pick-and-place tasks.
-
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.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere releases a 200-hour long-form egocentric dataset with persistent state tracking plus the STERA open infrastructure and processing pipeline to convert commodity mobile captures into training-ready fo...
-
World Action Models: A Survey
A survey that clarifies boundaries and organizes World Action Models by generation requirements and predictive substrates, identifying a trend toward generating less of the future.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.