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Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control
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Humanoid robots require both robust lower-body locomotion and precise upper-body manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body loco-manipulation policies, they lack precise manipulation with high DoF arms. In this paper, we propose decoupling upper-body control from locomotion, using inverse kinematics (IK) and motion retargeting for precise manipulation, while RL focuses on robust lower-body locomotion. We introduce PMP (Predictive Motion Priors), trained with Conditional Variational Autoencoder (CVAE) to effectively represent upper-body motions. The locomotion policy is trained conditioned on this upper-body motion representation, ensuring that the system remains robust with both manipulation and locomotion. We show that CVAE features are crucial for stability and robustness, and significantly outperforms RL-based whole-body control in precise manipulation. With precise upper-body motion and robust lower-body locomotion control, operators can remotely control the humanoid to walk around and explore different environments, while performing diverse manipulation tasks.
Forward citations
Cited by 12 Pith papers
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Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
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A reinforcement-learned time optimization policy that adaptively slows upper-body motion clips improves stability and precision of humanoid standing manipulation at a modest time cost.
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A simulated Unitree G1 humanoid learns to drum dozens of popular songs from MIDI with high F1 scores using a Rhythmic Contact Chain and temporal decomposition.
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GMT: General Motion Tracking for Humanoid Whole-Body Control
GMT trains a single unified humanoid policy using adaptive sampling and mixture-of-experts, achieving lower tracking errors than a re-implemented ExBody2 across diverse whole-body motions.
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Versatile Loco-Manipulation through Flexible Interlimb Coordination
ReLIC lets a robot dog dynamically reassign its legs between walking and manipulating, achieving 78.9% average success across 12 real-world loco-manipulation tasks.
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Hold My Beer: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control
A slow-fast two-agent reinforcement learning architecture with separate upper- and lower-body policies reduces end-effector shaking during humanoid locomotion.
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Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning
A VR-teleoperated, reinforcement-learning-balanced control stack lets a miniature ROBOTIS OP3 humanoid walk and manipulate objects simultaneously.
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REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
Jointly fine-tuning a diffusion-policy high-level planner and an RL low-level controller raises humanoid loco-manipulation success rates from ~50-70% to >90% in simulation.
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Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
A humanoid-specific multimodal occupancy perception system with a new dataset, sensor layout, and a fusion network that claims state-of-the-art results on its own benchmark.
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EMP: Executable Motion Prior for Humanoid Robot Standing Upper-body Motion Imitation
A state-conditioned executable motion prior network modifies upper-body motion targets so a humanoid can imitate human gestures while maintaining balance.
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AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation
AC-DiT adds mobility-to-body conditioning and perception-aware 2D/3D weighting to a diffusion transformer, improving success rates on simulated and real-world mobile manipulation tasks.
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SMAP: Self-supervised Motion Adaptation for Physically Plausible Humanoid Whole-body Control
SMAP uses a vector-quantized periodic autoencoder to adapt human motion into physically plausible humanoid motion, then distills an RL teacher policy into a student policy for whole-body control.
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