X2-N is a transformable wheel-legged humanoid robot with a reinforcement learning whole-body controller that enables dual-mode locomotion and manipulation across varied terrains.
Amass: Archive of motion capture as surface shapes
5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
SynAgent enables generalizable cooperative humanoid manipulation by transferring skills from solo human-object interactions to multi-agent scenarios via interaction-preserving retargeting, single-agent pretraining with multi-agent PPO, and a conditional VAE generative policy.
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.
RPG trains a single policy with transition and timing randomization for stable multi-skill fighting on humanoids, integrated with locomotion for arbitrary-duration combat.
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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X2-N: A Transformable Wheel-legged Humanoid Robot with Dual-mode Locomotion and Manipulation
X2-N is a transformable wheel-legged humanoid robot with a reinforcement learning whole-body controller that enables dual-mode locomotion and manipulation across varied terrains.
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SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy
SynAgent enables generalizable cooperative humanoid manipulation by transferring skills from solo human-object interactions to multi-agent scenarios via interaction-preserving retargeting, single-agent pretraining with multi-agent PPO, and a conditional VAE generative policy.
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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.
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RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting
RPG trains a single policy with transition and timing randomization for stable multi-skill fighting on humanoids, integrated with locomotion for arbitrary-duration combat.
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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.