A curated, searchable collection of robotics research with open-source implementations—so you can read, run, and reproduce.
Satoshi Yamamori, Koji Ishihara, Kentaro Minamikawa et al. · arXiv preprint · Jul 2026
Sim-to-real transfer in robot learning is often limited by discrepancies between the ideal actuator dynamics assumed during policy training and the nonlinear, hardware-dependent be...
Hexian Ni, Tao Lu, Yinghao Cai · ICML 2026 · Jul 2026
Reward design remains a central challenge in reinforcement learning (RL). Hand-crafted rewards are often difficult to specify and may lead to suboptimal policies, while learned rew...
Chenyang Ma, Yue Yang, Radu Corcodel et al. · arXiv preprint · Jul 2026
Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systematic study of real-scale bimanu...
Chi Zhang, Penglin Cai, Ziheng Xi et al. · arXiv preprint · Jul 2026
As an essential modality for dexterous and contact-rich tasks, tactile sensing provides precise force feedback that cannot be reliably inferred from vision. However, limited by har...
William English, Hao Zheng, Rickard Ewetz · arXiv preprint · Jul 2026
Vision-Language-Action (VLA) models have demonstrated promising generalization capabilities across robotic manipulation tasks, yet their real-world deployment remains limited by th...
Yuwan Liu, Hongze Yu, Song Liu et al. · arXiv preprint · Jul 2026
Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification. Prior work has incorporated ...
Yuquan Xue, Le Xu, Zeyi Liu et al. · arXiv preprint · Jul 2026
Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond t...
Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh et al. · arXiv · Jun 2026
We introduce ABC, a fully open-source stack for manipulation with behavior cloning. At its core is ABC-130K: the largest open-source teleoperation dataset to date, featuring 3,500 hours of data spanning over 130K episodes across 195 diverse tasks. Furthermore, we open-source o...
Junheng Li, Liang Wu, Sergio A. Esteban et al. · arXiv preprint · Jun 2026
In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole-body skills through large-scale simulation. However, using MPC inside RL often requires time-consuming problem construction or excessive training overhead, making such frameworks difficult to justify in practice. This work studies efficient training-time MPC guidance for humanoid locomotion and manipulation, termed MPC-RL. We introduce a centroidal-dynamics MPC reward formulation that leverages guidance from MPC trajectories in training time. To make this practical in massively parallel RL, we develop π^nMPC, a parallel-in-horizon and construction-free batched GPU MPC solver that operates directly on time-varying dynamics to avoid high memory usage and pre-compilation. Through a variety of comparative studies and hardware validations, we have found that MPC-RL achieves superior performance in locomotion and manipulation skills.
Jonathan Green, Jiaxu Xing, Nico Messikommer et al. · arXiv · Jun 2026
Autonomous drone racing is a fundamentally challenging regime for autonomous aerial robots, requiring time-optimal control while operating under persistent actuation saturation. While reinforcement learning (RL) has achieved human-level performance in this domain, current meth...
Yandong Wang, Jiaqian Yu, Xiongfeng Peng et al. · arXiv preprint · Jun 2026
Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end...
Yandong Wang, Jiaqian Yu, Xiongfeng Peng et al. · arXiv preprint · Jun 2026
Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end...
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