RL for Robotics

RL: Reinforcement Learning

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31 papers in the last four weeks, up 675% on the four weeks before. 0.3% of all new papers.

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Learning Acrobatic Flight from Preferences
Aug 26, 2025Colin Merk, Ismail Geles, Jiaxu Xing +3RL for Robotics Aerial Robotics
Robotics and Perception Group, University of Zurich, Switzerland · Autonomous Learning and Predictive Intelligence Lab, University of Zurich, Switzerland
RetrDex: Efficient Object Retrieval in Cluttered Scenes with a Dexterous Hand
Feb 25, 2025Fengshuo Bai, Yu Li, Jie Chu +5RL for Robotics Robotic Manipulation
Shanghai Jiao Tong University · PKU-PsiBot Joint Lab · Peking University +2
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
Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip
Jul 29, 2023Saki Omi, Hyo-Sang Shin, Namhoon Cho +2RL for Robotics Reinforcement Learning
Space Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg · School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, U.K. · Department of Aerospace Engineering, Seoul National University, Seoul, South Korea
Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids
Date pendingHongjin Chen, Wei Zhang, Pengfei Li +10RL for Robotics Humanoid Robot Control
Fudan University · TARS Robotics · Tsinghua University +3

Aug 26, 2025, cs.ROJ/K move · Enter open · S save

Learning Acrobatic Flight from Preferences

Colin Merk, Ismail Geles, Jiaxu Xing, Angel Romero+2

Robotics and Perception Group, University of Zurich, Switzerland · Autonomous Learning and Predictive Intelligence Lab, University of Zurich, Switzerland

RL for Robotics Aerial Robotics Reinforcement Learning Preference-Based RL

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Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where objectives are difficult to formalize or inherently subjective. Acrobatic flight poses a particularly challenging problem due to its complex dynamics, rapid movements, and the importance of precise execution. However, manually designed reward functions for such tasks often fail to capture the qualities that matter: we find that hand-crafted rewards agree with human judgment only 60.7% of the time, underscoring the need for preference-driven approaches. In this work, we propose Reward Ensemble under Confidence (REC), a probabilistic reward learning framework for PbRL that explicitly models per-timestep reward uncertainty through an ensemble of distributional reward models. By propagating uncertainty into the preference loss and leveraging disagreement for exploration, REC achieves 88.4% of shaped reward performance on acrobatic quadrotor control, compared to 55.2% with standard Preference PPO. We train policies in simulation and successfully transfer them zero-shot to the real world, demonstrating complex acrobatic maneuvers learned purely from preference feedback. We further validate REC on a continuous control benchmark, confirming its applicability beyond the domain of aerial robotics.

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Latest papers 155

Learning Acrobatic Flight from Preferences

RetrDex: Efficient Object Retrieval in Cluttered Scenes with a Dexterous Hand

A Survey on Reinforcement Learning Applications in SLAM

Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip

Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids