Research
Robotics Research Publications
An open index of robotics research papers, refreshed daily.
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papers in the database
LEARNING papers(27,419)
Clear ✕Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control
Runze Lin, Ziqi Zhuo, Junghui Chen +2 more
2026
Multi-Scale Control of Large Agent Populations: From Density Dynamics to Individual Actuation
Mario di Bernardo
2026
HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation
Xingyi Wang, Chenyun Zhang, Weiji Xie +4 more
2026
PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning
Yinfeng Gao, Qichao Zhang, Deqing Liu +8 more
2026
ViSA: Visited-State Augmentation for Generalized Goal-Space Contrastive Reinforcement Learning
Issa Nakamura, Tomoya Yamanokuchi, Yuki Kadokawa +5 more
2026
Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning
Mikoto Kudo, Takumi Tanabe, Akifumi Wachi +1 more
2026
EcoFair-CH-MARL: Scalable Constrained Hierarchical Multi-Agent RL with Real-Time Emission Budgets and Fairness Guarantees
Saad Alqithami
2026
Latent Dynamics-Aware OOD Monitoring for Trajectory Prediction with Provable Guarantees
Tongfei Guo, Lili Su
2026
A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study
Jingyi Liu, Jian Guo, Eberhard Gill
2026
Adapting Critic Match Loss Landscape Visualization to Off-policy Reinforcement Learning
Jingyi Liu, Jian Guo, Eberhard Gill
2026
Learning to Order: Task Sequencing as In-Context Optimization
Jan Kobiolka, Christian Frey, Arlind Kadra +2 more
2026
Visualizing Critic Match Loss Landscapes for Interpretation of Online Reinforcement Learning Control Algorithms
Jingyi Liu, Jian Guo, Eberhard Gill
2026
High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
Avik Kar, Siddharth Chandak, Rahul Singh +3 more
2026
Physics-Informed Policy Optimization via Analytic Dynamics Regularization
Namai Chandra, Liu Mohan, Zhihao Gu +1 more
2026
Predicting power grid frequency dynamics with invertible Koopman-based architectures
Eric Lupascu, Xiao Li, Benjamin Schäfer
2026
Chaos-Free Networks are Stable Recurrent Neural Networks
Stefano De Carli, Davide Previtali, Mirko Mazzoleni +1 more
2026
Amortizing Trajectory Diffusion with Keyed Drift Fields
Gokul Puthumanaillam, Melkior Ornik
2026
LLM-Guided Safe Reinforcement Learning for Energy System Topology Reconfiguration
Zongyan Zhang, Chao Shen, Xu Wan +2 more
2026
Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition
Seokmin Lee, Yunghee Lee, Byeonghyun Pak +1 more
2026
Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation
Mateo Haro, Julia Richter, Fan Yang +2 more
2026
Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics
Sylvester Kaczmarek
2026
Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving
Zhexi Lian, Haoran Wang, Xuerun Yan +4 more
2026
Your Vision-Language-Action Model Already Has Attention Heads For Path Deviation Detection
Jaehwan Jeong, Evelyn Zhu, Jinying Lin +5 more
2026
Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control
Grayson Lee, Minh Bui, Shuzi Zhou +3 more
2026