UniIntervene: Agentic model reduces human interventions in real-world robot RL by 57%
UniIntervene is a proposed agentic intervention model for human-in-the-loop reinforcement learning (HiL-RL) that autonomously detects unproductive exploration and recovers robot policies toward high-value states, replacing the bulk of human corrections. The system uses future-conditioned action-value estimation, a temporal value-risk critic, and a goal-conditioned recovery policy drawing from a memory of past interventions. Experiments on real-world robotic manipulation tasks show a 57% reduction in human interventions and an 8.6% improvement in average success rate over state-of-the-art HiL-RL baselines.
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AgenticRL: Self-refining LLM-guided reward design and policy refinement for UAV navigation
AgenticRL is a framework that uses a multimodal GPT agent to automate reward function generation, policy training via PPO, and closed-loop self-refinement for UAV navigation tasks. The agent evaluates trained policies through diagnostic feedback, identifies failure modes, and iteratively refines rewards without human intervention. Evaluated across five navigation tasks, the closed-loop refinement improves policy behavior by 71% over initial rewards, with sim-to-real transfer achieving 91% real-world success rate and 94% sim-to-real accuracy.
SafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLMs via RL-Driven Prompt Optimisation
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Agency-transferring technique improves RL policy training by bootstrapping from baseline policies
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HITL-D: Human-In-The-Loop Diffusion for Shared Control in Robotic Manipulation
HITL-D is a shared control framework that combines diffusion-based policies with human teleoperation for robotic manipulation tasks. The system autonomously updates end-effector orientation conditioned on scene point clouds and Cartesian position, reducing the number of joystick axes operators must manage. A 12-participant user study found 40% faster task completion, 37% lower perceived workload, and improved subjective ratings versus traditional teleoperation. The work addresses a relatively unexplored intersection of diffusion policy methods and human-in-the-loop control.
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Learning from Human Preferences: OpenAI and DeepMind Collaborate on Reward Learning from Comparisons
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Improving Model Safety Behavior with Rule-Based Rewards
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