arXiv Machine Learning

Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning

arXiv AI

Jun 17

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning

arXiv:2605. 05172v2 Announce Type: replace-cross Abstract: Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning.

By Lakshita Dodeja, Ondrej Biza, Shivam Vats, Stephen Hart, Stefanie Tellex, Robin Walters, Karl Schmeckpeper, Thomas Weng

reinforcement-learning robotics benchmarks

arXiv AI

Aug 18

Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning

arXiv:2608. 15088v1 Announce Type: cross Abstract: Human-in-the-loop (HIL) online reinforcement learning for real robots must absorb human interventions quickly while continuing to improve beyond the human prior.

By Zihang Wang, Yishan Wang

agents reinforcement-learning robotics

arXiv AI

Aug 13

TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning

arXiv:2605. 12236v2 Announce Type: replace-cross Abstract: Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.

By Matthew M. Hong, Jesse Zhang, Anusha Nagabandi, Abhishek Gupta

agents diffusion reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Aug 13

Adaptation of Generalist Robot Policies with Minimal Data

arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.

By Shreyas Kowshik, Sreyas Venkataraman, Leo Wang, Niharika Pant, Max Simchowitz, Aviral Kumar

agents reinforcement-learning robotics

arXiv AI

4d ago

The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction

arXiv:2608.22301v1 Announce Type: cross Abstract: Humans imitate at the level of intent: given a demonstration, we infer its goal and carry it out with whatever tools, objects, and layouts are at han...

By Xunzhe Zhou, Yiyang Cai, Fengyi Wang, Ran Ju, Hanxiang Ren, Ruizhe Liu, Yu Zhang, Qian Luo, Feng Chen, Pei Zhou, Yi Ma, Yanchao Yang

robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 11

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

By Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal, Shashwat Saxena, Jesse Zhang, Giri Anantharaman, Cleah Winston, Chaoyi Pan, Douglas Chen, Nai-Chieh Huang, Zeynep Temel, Oliver Kroemer, Sergey Levine, Abhishek Gupta, Hongkai Dai, Paarth Shah, Max Simchowitz

diffusion reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

arXiv AI

Aug 21

EXIMO: VLM Guided Exploration of VLA Policies

arXiv:2608. 19891v1 Announce Type: new Abstract: How to efficiently finetune robot policies to learn new tasks on the fly?

By Bhavya Sukhija, Oliver Groth, Mohit Shridhar, Tim Hertweck, Michael Bloesch, Markus Wulfmeier, Abbas Abdolmaleki, Martin Riedmiller

llms reinforcement-learning robotics fine-tuning multimodal

arXiv AI

Aug 19

Q-Learning With World Models

The paper introduces QWM, a framework that integrates world models with standard Q‑learning to perform test‑time search over imagined trajectories. By training the policy and value function solely on real transitions, QWM avoids compounding model bias while still benefiting from predictive search. Experiments on the Robomimic and LIBERO manipulation benchmarks show that QWM outperforms strong prior state‑of‑the‑art methods in both sample efficiency and performance.

By Perry Dong, Yueru Jia, Chelsea Finn, Dorsa Sadigh

reinforcement-learning robotics fine-tuning multimodal benchmarks safety

arXiv AI

Aug 10

AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies

arXiv:2608. 07065v1 Announce Type: cross Abstract: Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency by predicting short action sequences rather than single-step commands.

By Jinhe Tang, Weiming Zhi

rag robotics

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

arXiv AI

Aug 5

Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution

arXiv:2608. 03483v1 Announce Type: cross Abstract: Existing chunk-based Vision-Language-Action (VLA) models execute a fixed number of actions (i.

By Weichen Xu, Zhenhua Liu, Lin Luo, Yaobo Liang, Chengtang Yao, Qingyu Mei, Jian Cao, Xixin Cao, Xing Zhang, Jiaolong Yang, Baining Guo

reinforcement-learning robotics multimodal safety

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