arXiv Machine Learning

SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry

arXiv:2608. 09303v1 Announce Type: cross Abstract: The deployment of autonomous robotic systems in chemistry laboratories is accelerating experimental workflows and providing the foundational data for AI-driven scientific discovery.

arXiv AI

Jul 29

A context-adaptive policy framework for robust and reactive robotic manipulation via uncertainty-aware imitation learning

arXiv:2410. 24035v2 Announce Type: replace-cross Abstract: Generating robust and reactive manipulation strategies that can adapt to changing context information is a challenging task in robotics.

By Tim R. Winter, Leonard Kl\"upfel, Ashok M. Sundaram, Werner Friedl, Maximo A. Roa, Freek Stulp, Jo\~ao Silv\'erio

robotics benchmarks

arXiv AI

Jun 2

ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors

arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.

By Zifan Xu, Ran Gong, Maria Vittoria Minniti, Kausik Sivakumar, Ahmet Salih Gundogdu, Eric Rosen, Riedana Yan, Tushar Kusnur, Zixing Wang, Di Deng, Peter Stone, Xiaohan Zhang, Karl Schmeckpeper

llms diffusion reinforcement-learning robotics benchmarks

arXiv Machine Learning

Aug 11

Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

arXiv:2604. 26836v3 Announce Type: replace Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability.

By Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, Sebastian Trimpe

reinforcement-learning benchmarks safety

Hugging Face Trending Papers

Jul 8

Safe Reinforcement Learning using Ideas from Model Predictive Control

Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.

agents reinforcement-learning robotics safety

arXiv Machine Learning

Jul 9

Safe Reinforcement Learning using Ideas from Model Predictive Control

arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

agents reinforcement-learning robotics safety

arXiv AI

Jul 24

Expert Behavior Prior Reinforcement Learning

arXiv:2607. 21302v1 Announce Type: new Abstract: Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by leveraging policy priors derived from offline demonstrations.

By Gong Gao, Weidong Zhao, Xianhui Liu, Ning Jia

agents reinforcement-learning robotics benchmarks

arXiv AI

Jun 10

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

arXiv:2606. 10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains.

By Kaustubh Mani, Yann Pequignot, Vincent Mai, Liam Paull

agents reinforcement-learning safety

arXiv AI

Jun 8

Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control

arXiv:2512. 23292v5 Announce Type: replace Abstract: The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface.

By Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi, Sajedul Talukder, Diab Abueidda, Seid Koric, Souvik Chakraborty, Syed Bahauddin Alam

llms agents reinforcement-learning multimodal safety

arXiv AI

Jun 15

Sensitivity Shaping for Latent Modeling

arXiv:2606. 14585v1 Announce Type: cross Abstract: Generative dynamics models enable planning in challenging robotic systems, but safe deployment requires reliably detecting policy-induced out-of-distribution (OOD) transitions.

By Hongzhan Yu, Chenghao Li, Ruipeng Zhang, Henrik Christensen, Sicun Gao

robotics

arXiv Machine Learning

Jun 2

All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning

arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.

By Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele, Friedrich Solowjow, Sebastian Trimpe

agents reinforcement-learning robotics

arXiv AI

Jun 2

Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry

arXiv:2606. 01098v1 Announce Type: cross Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control.

By Zemin Yang, Yaoyu He, Yiming Zhong, Yuhao Zhang, Xinge Zhu, Yao Mu, Qingqiu Huang, Yuexin Ma

diffusion robotics efficiency

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.

By Raymond Yu, William Huey, Mustafa Mukadam, Anusha Nagabandi, Abhishek Gupta

reinforcement-learning robotics efficiency

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