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.
arXiv preprint arXiv:2602.06643 (2026)
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2026 4representative citing papers
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.
A multi-agent LLM framework for humanoid loco-manipulation that separates active spatial perception and task planning from generalizable action generation without task-specific real-robot data.
HTD, a multimodal transformer policy trained with behavioral cloning and touch dreaming to predict future tactile latents, achieves a 90.9% relative success rate improvement over baselines on five real-world contact-rich humanoid loco-manipulation tasks.
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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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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Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum
A multi-agent LLM framework for humanoid loco-manipulation that separates active spatial perception and task planning from generalizable action generation without task-specific real-robot data.
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Learning Versatile Humanoid Manipulation with Touch Dreaming
HTD, a multimodal transformer policy trained with behavioral cloning and touch dreaming to predict future tactile latents, achieves a 90.9% relative success rate improvement over baselines on five real-world contact-rich humanoid loco-manipulation tasks.