Being-H0.7 adds future-aware latent reasoning to direct VLA policies via dual-branch alignment on latent queries, matching world-model benefits at VLA efficiency.
Amo: Adaptive motion optimization for hyper-dexterous humanoid whole-body control.arXiv preprint arXiv:2505.03738
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ROSA introduces shared GPU-pool serving, robotics-aware abstractions for multi-model pipelines, and factory-productivity scheduling that improves output by up to 12.06x over dedicated per-robot systems.
MPC-based retargeting framework enables cross-morphology whole-body teleoperation from a single XR device via dynamic feasibility optimization, state synchronization, and SLAM feedback, with reported gains in simulation and real-world tests.
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.
HAIC enables robust humanoid interactions with underactuated objects by predicting their dynamics from proprioceptive history and using a world model for adaptive control.
Scaling motion tracking models along size, data volume, and compute produces a foundation model for natural, robust humanoid whole-body control with downstream uses in kinematic planning and vision-language-action models.
MPC-RL combines a centroidal-dynamics MPC reward with a batched GPU solver (π^n MPC) to accelerate RL training for humanoid locomotion and manipulation tasks.
DREAM is a mobile manipulation system that constructs online spatio-semantic voxel memory with redundancy-aware pruning and hybrid language-vision localization, reporting higher long-horizon success rates than DynaMem in dynamic lab scenes.
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.
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.
A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.
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