Robotic RL

RL: Reinforcement Learning

Robotic reinforcement learning studies how robots can learn actions from rewards and experience in tasks such as locomotion, navigation, and contact-rich manipulation. Common approaches include model-free methods such as PPO and SAC, hierarchical and model-based control, and reinforcement-learning fine-tuning of policies pretrained from demonstrations or large datasets. Simulation and parallel training make it possible to explore many behaviors, while domain randomization, system identification, and real-robot fine-tuning help transfer policies to hardware. A central challenge is achieving sample-efficient learning without sacrificing safety, robustness, or performance when sensors, dynamics, and task conditions differ from training.

Latest papers

307 papers49 papers in the last four weeks, up 14% on the four weeks before. 0.3% of all new papers.

HERB: Human-augmented Efficient Reinforcement learning for Bin-packing
Apr 23, 2025Gojko Perovic, Nuno Ferreira Duarte, Atabak Dehban +3Robotic RL Robotics
BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy, and with the Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy. · Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.
CONTHER: Context-Aware Reinforcement Learning for Robotic Manipulation with Sparse Rewards
Mar 20, 2025Maria Makarova, Qian Liu, Dzmitry TsetserukouReinforcement Learning Experience Replay
Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia · Department of Computer Science and Technology, Dalian University of Technology, China
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Mar 19, 2025Kevin Wang, Ishaan Javali, Michał Bortkiewicz +2Reinforcement Learning Self-Supervised RL
Princeton University · Warsaw University of Technology · Warsaw University of Technology, Tooploox, IDEAS Research Institute
Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Mar 1, 2025Heng Zhang, Gokhan Solak, Sebastian Hjorth +1Robotic Control Contact-Rich Robotic Manipulation
HRI2 Lab, Istituto Italiano di Tecnologia, Genoa, Italy · Ph.D. program of national interest in Robotics and Intelligent Machines (DRIM) and University of Genova, Genoa, Italy
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
Oct 27, 2024Kaiyan Zhao, Yiming Wang, Yuyang Chen +3Experience Replay Determinantal Point Process
School of Computer Science, Wuhan University, Wuhan, China · State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao, China · School of Artificial Intelligence Shenzhen Polytechnic University, China
A Survey on Reinforcement Learning Applications in SLAM
Aug 26, 2024Mohammad Dehghani Tezerjani, Mohammad Khoshnazar, Mohammadhamed Tangestanizadeh +2Simultaneous Localization and Mapping RL for Robotics
Computer Science and Engineering, University of North Texas, Denton, USA · Institute of Artificial Intelligence, University of Bremen, Bremen, Germany · Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, USA +1
Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertainty
Apr 22, 2024Xulin Chen, Ruipeng Liu, Zhenyu Gan +1Robotic RL Robust RL
College of Engineering & Computer Science, Syracuse University

Apr 23, 2025J/K move · Enter open · S save

HERB: Human-augmented Efficient Reinforcement learning for Bin-packing

Gojko Perovic, Nuno Ferreira Duarte, Atabak Dehban, Gonçalo Teixeira+2

BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy, and with the Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy. · Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.

Robotic RL Robotics Robotic Manipulation Learning from Demonstration

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Packing objects efficiently is a fundamental problem in logistics, warehouse automation, and robotics. When dealing with highly diverse 3D objects (household or grocery items), closed-form solutions are infeasible, and heuristic or model-free Reinforcement Learning~(RL) methods tend to focus solely on geometric optimization, relying on exhaustive searches of the discretized solution space. This leads to long training times (for pure RL) and high latency (heuristics), limited transferability to robotic scenarios, and ultimately ignores object characteristics (fragility, deformability) and human preferences. We propose HERB, a human-augmented RL framework for packing irregular objects, the first to explore the potential of learning from human demonstrations to solve this complex task. It leverages human demonstrations of packing strategies, which inherently exhibit latent factors such as space optimization, stability, and object properties that are difficult to model explicitly. The human-expert data is combined with RL exploration to provide the placement of each object inside the container. Experimental results show that our method outperforms heuristic, purely RL-based, and imitation learning approaches in packing efficiency and latency. Qualitative results highlight that our packing strategy produces more stable, human-like arrangements, which we expect to be more appropriate and widely accepted. Finally, we demonstrate the real-world feasibility of our method on a robotic system.