UniIntervene uses future-conditioned action-value estimation and a temporal value-risk critic to trigger memory-based recovery interventions, reporting 8.6% higher success rates and 57% fewer human interventions than prior HiL-RL methods on real manipulation tasks.
Conservative safety critics for exploration
6 Pith papers cite this work, alongside 32 external citations. Polarity classification is still indexing.
6
Pith papers citing it
32
external citations · external index
years
2026 6verdicts
UNVERDICTED 6representative citing papers
SHAPO adds a sharpness-aware adjustment to policy optimization that reweights gradients to favor conservative behavior in uncertain areas, yielding better safety-performance tradeoffs on continuous control tasks.
A model-free RL method arbitrates between a functional baseline policy and a learning policy, transferring agency over time to yield a standalone policy with high goal-reaching rates and competitive returns on continuous-control tasks.
Safe-Support Q-Learning trains Q-functions and policies in reinforcement learning without ever visiting unsafe states by constraining the behavior policy to a safe set and using KL-regularized Bellman targets in a two-stage framework.
Pre-VLA is a multimodal runtime verifier that predicts safety confidence and advantage scores for action chunks, raising closed-loop success rates on the LIBERO benchmark from 30.79% to 37.62%.
A structured literature survey of safety mechanisms in long-horizon robotic manipulation organized by intervention timing and strength of supporting evidence.
citing papers explorer
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UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
UniIntervene uses future-conditioned action-value estimation and a temporal value-risk critic to trigger memory-based recovery interventions, reporting 8.6% higher success rates and 57% fewer human interventions than prior HiL-RL methods on real manipulation tasks.
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SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
SHAPO adds a sharpness-aware adjustment to policy optimization that reweights gradients to favor conservative behavior in uncertain areas, yielding better safety-performance tradeoffs on continuous control tasks.
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An Agency-Transferring Model-Free Policy Enhancement Technique
A model-free RL method arbitrates between a functional baseline policy and a learning policy, transferring agency over time to yield a standalone policy with high goal-reaching rates and competitive returns on continuous-control tasks.
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Safe-Support Q-Learning: Learning without Unsafe Exploration
Safe-Support Q-Learning trains Q-functions and policies in reinforcement learning without ever visiting unsafe states by constraining the behavior policy to a safe set and using KL-regularized Bellman targets in a two-stage framework.
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Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rollouts
Pre-VLA is a multimodal runtime verifier that predicts safety confidence and advantage scores for action chunks, raising closed-loop success rates on the LIBERO benchmark from 30.79% to 37.62%.
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Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation
A structured literature survey of safety mechanisms in long-horizon robotic manipulation organized by intervention timing and strength of supporting evidence.