Selective AMP in RL enables a single policy for five humanoid gaits with faster convergence and better performance on stability tasks without losing dynamic agility.
https://arxiv.org/abs/2404.17070
5 Pith papers cite this work. Polarity classification is still indexing.
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DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
A lightweight RL framework trains terrain-agnostic 3D foothold-tracking policies for humanoids that transfer directly to real-world use as standalone low-level controllers.
A literature review of pHHI that proposes a taxonomy of interaction types by modality and engagement level while outlining pathways to integrate control, intent, and modeling for more seamless humanoid-human collaboration.
Simulation comparison shows active toes reduce cost of transport by 17.5%, heel-strike GRF by 5.0%, and improve agility metrics versus toe ablation in a biped robot.
citing papers explorer
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Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior
Selective AMP in RL enables a single policy for five humanoid gaits with faster convergence and better performance on stability tasks without losing dynamic agility.
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DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
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Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
A lightweight RL framework trains terrain-agnostic 3D foothold-tracking policies for humanoids that transfer directly to real-world use as standalone low-level controllers.
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Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next
A literature review of pHHI that proposes a taxonomy of interaction types by modality and engagement level while outlining pathways to integrate control, intent, and modeling for more seamless humanoid-human collaboration.
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Comparative Study on Agility, Efficiency, and Impact Absorption of Bipedal Robots with Active Toes
Simulation comparison shows active toes reduce cost of transport by 17.5%, heel-strike GRF by 5.0%, and improve agility metrics versus toe ablation in a biped robot.