arXiv AI
Iterative Policy Refinement through Semantic Rollout Analysis
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Sep 21
DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
DexPIE is a post‑training framework that improves dexterous manipulation policies using real‑world experience. It introduces a dexterous‑hand‑adapted intervention system and multi‑stage DAgger‑style data collection to enhance exploration, aligns training and inference to reduce distribution shift, and conditions the policy on a continuous optimality indicator for fine‑grained data quality use. In three real‑world tasks, DexPIE boosts success rates by 37.3% over a demonstration‑based baseline, outperforming all other methods and showing stronger robustness.
arXiv AI
Jul 2
Language-Critique Imitation Learning from Suboptimal Demonstrations
arXiv:2607. 01225v1 Announce Type: cross Abstract: Prior work on imitation learning from suboptimal demonstrations typically relies on compressed supervision signals such as confidence estimates, discriminator scores, or importance weights.
arXiv Computer Vision
4d ago
Multi-Granularity Language-Guided Imitation Learning via Instruction Decomposition
arXiv:2609.37135v1 Announce Type: new Abstract: Using language instructions as conditions to guide robot policy learning has recently become an important research domain. However, existing language-g...
arXiv AI
Jun 2
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
arXiv:2606. 00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics.
arXiv AI
Jul 10
Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning
arXiv:2607. 07859v1 Announce Type: new Abstract: Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn behaviors adhering to human values.
arXiv AI
Jun 17
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
arXiv:2606. 18247v1 Announce Type: cross Abstract: Robots deployed in the real world should learn from their experience and improve over time.