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HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots
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Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-manipulation, and tabletop manipulation, each demanding a different mode of control. For example, navigation relies on root velocity tracking, while tabletop manipulation prioritizes upper-body joint angle tracking. Existing approaches typically train individual policies tailored to a specific command space, limiting their transferability across modes. We present the key insight that full-body kinematic motion imitation can serve as a common abstraction for all these tasks and provide general-purpose motor skills for learning multiple modes of whole-body control. Building on this, we propose HOVER (Humanoid Versatile Controller), a multi-mode policy distillation framework that consolidates diverse control modes into a unified policy. HOVER enables seamless transitions between control modes while preserving the distinct advantages of each, offering a robust and scalable solution for humanoid control across a wide range of modes. By eliminating the need for policy retraining for each control mode, our approach improves efficiency and flexibility for future humanoid applications.
Forward citations
Cited by 13 Pith papers
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Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
A taxonomy of robot learning on a weights-versus-skills axis, with a five-rung self-improvement ladder whose top cell (feedback plus memory plus search) holds only a few recent systems.
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ContactMimic: Humanoid Object Interaction via Contact Control
A humanoid tracking policy is trained with contact-following rewards and trajectory augmentation to decouple physical contact from keypoint geometry, enabling runtime contact control.
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Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization
A physics-aware motion-retargeting pipeline that uses ground-reaction-force-derived heel-toe contacts produces dynamically feasible humanoid references and improves downstream imitation learning.
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Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
Gaze-guided foveated patch tokenization reduces ViT tokens by 94%, accelerates training 7x and inference 3x, and improves robustness to distractors in bimanual manipulation policies.
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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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From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
BumbleBee, an expert-to-generalist pipeline using autoencoder-based motion clustering and per-cluster delta action models, reports state-of-the-art whole-body control on a Unitree G1 humanoid, with success rates of 89...
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SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending
SkillBlender pretrains reusable goal-conditioned skills and blends them with softmax per-joint weights to solve simulated humanoid loco-manipulation tasks with one or two reward terms.
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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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Tired Actor: Fatigue-Informed Character Control
Injecting a muscle-fatigue model into a general physics-based character controller preserves motion imitation accuracy while producing tired, more human-like behaviors such as shorter steps, corner cutting, and fall c...
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Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
A single diffusion-plus-residual-RL policy, trained on four robot morphologies using zero-padded observations and actions, outperforms per-robot PPO baselines in simulation and transfers to real robots.
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Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
In a confounded single-seed comparison, a dual-critic design reached targets 3.5x faster than a unified critic, but the causal role of critic architecture was not isolated.
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From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control
A new 124K-clip dataset with hierarchical text annotations, plus a pipeline that couples an LLM planner, a text-to-pose VAE, diffusion in-betweening, and physics control to generate long-horizon human behaviors.
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RSL-RL: A Learning Library for Robotics Research
RSL-RL is a compact, GPU-accelerated open-source RL library for robotics, providing PPO, DAgger-style behavior cloning, and auxiliary techniques in an easily modifiable codebase.
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