EgoTL provides a new egocentric dataset with think-aloud chains and metric labels that benchmarks VLMs on long-horizon tasks and improves their planning, reasoning, and spatial grounding after finetuning.
Egovid-5m: A large-scale video-action dataset for egocentric video generation
8 Pith papers cite this work. Polarity classification is still indexing.
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SFHand presents the first streaming language-guided autoregressive framework for 3D hand forecasting, achieving up to 35.8% gains over prior methods and 13.4% better downstream embodied task performance.
E³C is a video diffusion model that disentangles persistent 3D scene structure via point-cloud memory from human dynamics via ego-exo pose controls for improved egocentric video generation on the Nymeria dataset.
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.
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.
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.
The survey organizes human-video-based robot learning into task-, observation-, and action-oriented transfer pathways, reviews associated datasets, and outlines challenges for scalable embodied AI.
citing papers explorer
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EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks
EgoTL provides a new egocentric dataset with think-aloud chains and metric labels that benchmarks VLMs on long-horizon tasks and improves their planning, reasoning, and spatial grounding after finetuning.
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SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting
SFHand presents the first streaming language-guided autoregressive framework for 3D hand forecasting, achieving up to 35.8% gains over prior methods and 13.4% better downstream embodied task performance.
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E$^3$C: Video Generation with 3D Environmental Memory and Ego-Exo Human Pose Control
E³C is a video diffusion model that disentangles persistent 3D scene structure via point-cloud memory from human dynamics via ego-exo pose controls for improved egocentric video generation on the Nymeria dataset.
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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.
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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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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.
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Robot Learning from Human Videos: A Survey
The survey organizes human-video-based robot learning into task-, observation-, and action-oriented transfer pathways, reviews associated datasets, and outlines challenges for scalable embodied AI.
- HandsOnWorld: Unconstrained Egocentric Video Generation with Camera-Disentangled Hand Control