The paper introduces SP-VTP as a new setting for egocentric manipulation, releases the EgoSPT dataset with first-frame spatial annotations, and proposes the SPOT model that outperforms non-prompted baselines on cross-scene trajectory prediction.
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arXiv preprint arXiv:2602.06643 (2026)
14 Pith papers cite this work. Polarity classification is still indexing.
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2026 14representative citing papers
OpenHLM is an empirical recipe yielding a whole-body humanoid VLA model that outperforms GR00T N1.6 and Ψ0 baselines on long-horizon tasks using less than half the demonstration time.
LEGS shows synthetic data from a 3DGS-mesh hybrid simulator trains VLA policies for humanoid pick-and-place that match or exceed human teleoperation performance across multiple backbones and tasks while enabling low-cost robustness to appearance shifts.
BifrostUMI enables robot-free human demonstration capture via VR and wrist cameras to train visuomotor policies that predict keypoint trajectories for transfer to humanoid whole-body control through retargeting.
Robot-free VR–UMI demos of sparse whole-body keypoints can be retargeted and executed as deployable Unitree G1 whole-body skills more efficiently than teleoperation.
DUET pretrains collaborative policies on human-human VR demonstrations then fine-tunes on minimal robot teleoperation data, achieving equal or better performance than robot-only baselines with 5.4x faster collection across four tasks.
HALOMI extends UMI with egocentric sensing and a manifold-constrained controller plus alignment adaptations to learn loco-manipulation on humanoids from human demos, reporting 85% average success on three real-world tasks.
OMG is a diffusion model for omni-modal whole-body humanoid motion generation that uses language, audio, and reference motions after large-scale data curation to achieve state-of-the-art performance and adaptation.
OASIS generates scalable simulation data for humanoid loco-manipulation via 3D generative asset reconstruction and domain randomization, yielding a policy with higher zero-shot real-world success than real-robot teleoperation data.
HANDOFF is a distilled mixture-of-experts humanoid whole-body controller that follows a compact task-space interface, matches SOTA velocity tracking, provides large manipulation workspace on Unitree G1, and supports VLM-driven agentic planning with no task-specific data.
GRAIL creates over 20,000 synthetic loco-manipulation sequences from known 3D configurations and video priors, then trains policies that achieve 84% pick-up and 90% stair-climbing success on a real Unitree G1 humanoid using only the generated data.
A multi-agent large-model framework (Active Spatial Brain + Generalizable Action Cerebellum) enables spatial-aware humanoid whole-body manipulation without task-specific real-robot data.
An asynchronous upper-body task-space tracking policy for humanoids is learned via teacher-student distillation with sliding-window rewards and MPC post-training, showing better low-rate tracking than baselines in simulation and on Unitree G1 hardware.
citing papers explorer
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Spatially Prompted Visual Trajectory Prediction for Egocentric Manipulation
The paper introduces SP-VTP as a new setting for egocentric manipulation, releases the EgoSPT dataset with first-frame spatial annotations, and proposes the SPOT model that outperforms non-prompted baselines on cross-scene trajectory prediction.
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OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
OpenHLM is an empirical recipe yielding a whole-body humanoid VLA model that outperforms GR00T N1.6 and Ψ0 baselines on long-horizon tasks using less than half the demonstration time.
-
LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World
LEGS shows synthetic data from a 3DGS-mesh hybrid simulator trains VLA policies for humanoid pick-and-place that match or exceed human teleoperation performance across multiple backbones and tasks while enabling low-cost robustness to appearance shifts.
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BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
BifrostUMI enables robot-free human demonstration capture via VR and wrist cameras to train visuomotor policies that predict keypoint trajectories for transfer to humanoid whole-body control through retargeting.
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HumanoidUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
Robot-free VR–UMI demos of sparse whole-body keypoints can be retargeted and executed as deployable Unitree G1 whole-body skills more efficiently than teleoperation.
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Duet: Dual-Robot Understanding via Efficient Teaching
DUET pretrains collaborative policies on human-human VR demonstrations then fine-tunes on minimal robot teleoperation data, achieving equal or better performance than robot-only baselines with 5.4x faster collection across four tasks.
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HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations
HALOMI extends UMI with egocentric sensing and a manifold-constrained controller plus alignment adaptations to learn loco-manipulation on humanoids from human demos, reporting 85% average success on three real-world tasks.
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OMG: Omni-Modal Motion Generation for Generalist Humanoid Control
OMG is a diffusion model for omni-modal whole-body humanoid motion generation that uses language, audio, and reference motions after large-scale data curation to achieve state-of-the-art performance and adaptation.
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OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation
OASIS generates scalable simulation data for humanoid loco-manipulation via 3D generative asset reconstruction and domain randomization, yielding a policy with higher zero-shot real-world success than real-robot teleoperation data.
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HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
HANDOFF is a distilled mixture-of-experts humanoid whole-body controller that follows a compact task-space interface, matches SOTA velocity tracking, provides large manipulation workspace on Unitree G1, and supports VLM-driven agentic planning with no task-specific data.
-
GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors
GRAIL creates over 20,000 synthetic loco-manipulation sequences from known 3D configurations and video priors, then trains policies that achieve 84% pick-up and 90% stair-climbing success on a real Unitree G1 humanoid using only the generated data.
-
Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum
A multi-agent large-model framework (Active Spatial Brain + Generalizable Action Cerebellum) enables spatial-aware humanoid whole-body manipulation without task-specific real-robot data.
-
Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots
An asynchronous upper-body task-space tracking policy for humanoids is learned via teacher-student distillation with sliding-window rewards and MPC post-training, showing better low-rate tracking than baselines in simulation and on Unitree G1 hardware.
- Learning Versatile Humanoid Manipulation with Touch Dreaming