arXiv Machine Learning

SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning

arXiv:2511. 05355v3 Announce Type: replace Abstract: Flow matching (FM) has shown promising results in data-driven planning.

arXiv AI

Jun 12

PolyFlow: Safe and Efficient Polytope-Constrained Flow Matching with Constraint Embedding and Projection-free Update

arXiv:2606. 13400v1 Announce Type: cross Abstract: While flow-based generative models have demonstrated strong performance across a wide range of domains, deploying them in safety-critical physical systems remains challenging due to strict constraint requirements.

By Jianming Ma, Qiyue Yang, Yang Zhang, Liyun Yan, Zhanxiang Cao, Yazhou Zhang, Yue Gao

rag diffusion efficiency benchmarks safety

arXiv AI

Jul 17

ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation

arXiv:2607. 14424v1 Announce Type: cross Abstract: In recent years Flow Matching has become a prominent method for generative modeling robot motion generation.

By Nutan Chen, Jianxiang Feng, Marvin Alles, Botond Cseke

diffusion robotics

arXiv Machine Learning

Jun 9

Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions

arXiv:2601. 22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical.

By Lingkai Kong, Anagha Satish, Hezi Jiang, Akseli Kangaslahti, Andrew Ma, Wenbo Chen, Mingxiao Song, Lily Xu, Milind Tambe

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jul 17

Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows

arXiv:2607. 14272v1 Announce Type: new Abstract: Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retraining.

By Jingdong Zhang, Xinze Li, Yize Jiang, Luan Yang, Minkai Xu, Junhong Liu

diffusion reinforcement-learning benchmarks

arXiv AI

Jul 7

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs

arXiv:2607. 04854v1 Announce Type: new Abstract: Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications.

By Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Aug 4

Provably Safe Generative Sampling with Constricting Barrier Functions

arXiv:2602. 21429v3 Announce Type: replace Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions.

By Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti

diffusion robotics safety

arXiv AI

Jun 6

From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution

arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.

By Lidor Erez, Shahaf S. Shperberg, Ayal Taitler

agents reinforcement-learning robotics

Hugging Face Trending Papers

Jul 6

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs

Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process.

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 30

Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback

arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.

By Jiamei Jiang, Jiajing Zhang, Feifei Mo, Linjing Li, Daniel Zeng

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 16

Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC

arXiv:2606. 15594v1 Announce Type: cross Abstract: We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models.

By Devesh Nath, Anutam Srinivasan, Haoran Yin, Ruitong Jiang, Jeffrey Fang, Glen Chou

rag safety

arXiv AI

1d ago

Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models

arXiv:2604. 07084v2 Announce Type: replace-cross Abstract: Open-loop end-to-end neural motion planners have recently been proposed to improve motion planning for robotic manipulators.

By Davood Soleymanzadeh, Xiao Liang, Minghui Zheng

diffusion robotics benchmarks

arXiv Machine Learning

2d ago

hint$^2$: Hierarchical World Models for Inference-Time Temporal Logic Guidance

arXiv:2608. 13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime.

By Moritz Zoellner, Anastasios Manganaris, Ahmed H. Qureshi, Rohan Paleja

diffusion robotics safety

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