arXiv AI
Jul 14
VINE: Taming Generative Control Policies for Reinforcement Learning
arXiv:2607. 10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning.
VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching
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arXiv Machine Learning
Jul 9
Latent Policy Steering through One-Step Flow Policies
arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.
arXiv Machine Learning
Jun 8
GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios
arXiv:2606. 06967v1 Announce Type: new Abstract: Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous-control tasks.
arXiv AI
Jun 10
Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
arXiv:2606. 11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control.
arXiv AI
Oct 19
Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors
The paper introduces AnchorQ, a method that improves sample-efficient reinforcement learning for policy refinement by using flow-reversed action anchors. AnchorQ maps reference behaviors to state-conditioned latent anchors, samples local particles around these anchors, and employs value-guided sampling to explore promising refinements. Experiments on simulated and real-world manipulation tasks show that AnchorQ outperforms state-of-the-art post-training baselines, achieving a 37% higher real-world success rate and 97% greater simulated task throughput within the same interaction budget.
arXiv Machine Learning
Jun 11
OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.
arXiv Machine Learning
Oct 22
Doob-Based Generative Policy Optimization
Doob-Based Generative Policy Optimization (DoobPO) introduces a likelihood‑free framework for fine‑tuning generative policies such as diffusion and flow models. It first learns a lightweight ratio model to represent the desired policy update and then distills this ratio‑tilted target policy into the generative policy via conditional drift matching. The method supports standard policy improvement rules like PPO and GRPO, proves gradient equivalence to an ideal Doob drift matching objective, and demonstrates strong performance on MuJoCo continuous‑control benchmarks.
arXiv Machine Learning
Jun 8
SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows
arXiv:2602. 09580v4 Announce Type: replace-cross Abstract: Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimodal action distributions.
arXiv Computer Vision
Aug 21
RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.
arXiv Machine Learning
Jun 29
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.
arXiv AI
Jun 9
Reinforcement Learning for Flow-Matching Policies with Density Transport
arXiv:2606. 08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems.
Hugging Face Trending Papers
Aug 20
RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation
Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction. Yet prevalent diffusion- and flow-matching robot policies lack tractable likelihoods, limiting their use in likelihood-based offline RL post-training.