A post-hoc predictive safety filter adjusts RL policy contact locations for quadruped robots via sampling-based optimization on a full-physics model, reducing safety violations in cluttered environments with minimal performance deviation.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
HORIZON is a recoverability-governed checkpointed frontier curriculum for on-policy physical-domain scaling on quadruped locomotion that identifies three regularities: uneven widening, non-monotonic composition, and the necessity of joint on-policy interaction.
LEAP enables real-time proprioceptive adaptation to unseen damage in a 6DoF soft wrist using HSA actuators by combining latent damage representations with a robust ensemble method, with conditions identified for linear rather than exponential sample complexity.
TAM is a policy-agnostic torque adaptation module trained in randomized simulation that improves zero-shot real-robot performance on dynamic manipulation tasks compared to system identification and RMA baselines.
citing papers explorer
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Shield-Loco: Shielding Locomotion Policies with Predictive Safety Filtering
A post-hoc predictive safety filter adjusts RL policy contact locations for quadruped robots via sampling-based optimization on a full-physics model, reducing safety violations in cluttered environments with minimal performance deviation.
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HORIZON: Recoverability-Governed Curriculum for Physical-Domain Scaling
HORIZON is a recoverability-governed checkpointed frontier curriculum for on-policy physical-domain scaling on quadruped locomotion that identifies three regularities: uneven widening, non-monotonic composition, and the necessity of joint on-policy interaction.
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Damage Adaptation in Seconds for Architected Materials
LEAP enables real-time proprioceptive adaptation to unseen damage in a 6DoF soft wrist using HSA actuators by combining latent damage representations with a robust ensemble method, with conditions identified for linear rather than exponential sample complexity.
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TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation
TAM is a policy-agnostic torque adaptation module trained in randomized simulation that improves zero-shot real-robot performance on dynamic manipulation tasks compared to system identification and RMA baselines.