State-dependent adversarial motion priors with a gravity-threshold gate enable one frozen policy to unify walking, running, and recovery on humanoid hardware.
Safefall: Learning protective control for humanoid robots
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.RO 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
CMP projects actions onto a learned competence manifold using a frame-wise safety scheme and isomorphic latent space to achieve up to 10x better survival in out-of-distribution scenarios with under 10% tracking loss.
ConstrainedMimic integrates operational space control and control barrier functions into RL tracking policies to enforce arbitrary runtime constraints on humanoid kinematics and dynamics while preserving contact modes and tracking goals.
A single causal-transformer policy with latent recovery modes and contact-affordance prediction enables humanoid robots to recover from 100-300 N pushes with 100% success in simulation, generalizing zero-shot across wall distances, mass, friction, and latency changes.
citing papers explorer
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Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors
State-dependent adversarial motion priors with a gravity-threshold gate enable one frozen policy to unify walking, running, and recovery on humanoid hardware.
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CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection
CMP projects actions onto a learned competence manifold using a frame-wise safety scheme and isomorphic latent space to achieve up to 10x better survival in out-of-distribution scenarios with under 10% tracking loss.
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Constrained Whole-Body Tracking for Humanoid Robots
ConstrainedMimic integrates operational space control and control barrier functions into RL tracking policies to enforce arbitrary runtime constraints on humanoid kinematics and dynamics while preserving contact modes and tracking goals.
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RecoverFormer: End-to-End Contact-Aware Recovery for Humanoid Robots
A single causal-transformer policy with latent recovery modes and contact-affordance prediction enables humanoid robots to recover from 100-300 N pushes with 100% success in simulation, generalizing zero-shot across wall distances, mass, friction, and latency changes.