Three papers from RLLAB are accepted to ICRA 2026

[2026.02.01]

Following papers are accepted to the IEEE International Conference on Robotics and Automation (ICRA 2026): 

• Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangement by Hogun Kee, Wooseok Oh, Minjae Kang, Hyemin Ahn, and Songhwai Oh
  

  • Abstract: In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D camera. We address two major problems for tabletop tidying up problem: (1) the lack of public datasets and benchmarks, and (2) the difficulty of specifying the goal configuration of unseen objects. We address the former by presenting the tabletop tidying up (TTU) dataset, a structured dataset collected in simulation. Using this dataset, we train a vision-based discriminator capable of predicting the tidiness score. This discriminator can consistently evaluate the degree of tidiness across unseen configurations, including real-world scenes. Addressing the second problem, we employ Monte Carlo tree search (MCTS) to find tidying trajectories without specifying explicit goals. Instead of providing specific goals, we demonstrate that our MCTS-based planner can find diverse tidied configurations using the tidiness score as a guidance. Consequently, we propose TSMCTS, which integrates a tidiness discriminator with an MCTS-based tidying planner to find optimal tidied arrangements. TSMCTS has successfully demonstrated its capability across various environments, including coffee tables, dining tables, office desks, and bathrooms. The TTU dataset and code will be publicly available.
  • Video

• Playbook: Scalable Discrete Skill Discovery from Unstructured Datasets for Long-Horizon Decision-Making Problems by Minjae Kang, Mineui Hong, and Songhwai Oh 
  

  • Abstract: Skill discovery methods enable agents to tackle intricate tasks by acquiring diverse and useful skills from task-agnostic datasets in an unsupervised manner. To apply these methods to more general and everyday tasks, the skill set must be scalable. However, current approaches struggle with this scalability, often facing the challenge of catastrophic forgetting when learning new skills. To address this limitation, we propose a scalable skill discovery algorithm, a playbook, which can accommodate unseen tasks by acquiring new skills while maintaining previously learned ones. The scalable structure of the playbook, consisting of finite and independent plays and primitives, enables expansion by adding new elements to accommodate new tasks. The proposed method is evaluated in the complex robotic manipulation benchmarks, and the results show that the playbook outperforms existing state-of-the-art methods.
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• Memory-Efficient Voxelized Renderable Neural 3D Spatial Representation for Vision-Based Robotics by Howoong Jun, Seongbo Ha, Jaewon Lee, Hyeonwoo Yu, and Songhwai Oh
  

  • Abstract: In this paper, we introduce a novel approach for modeling a memory-efficient spatial representation with 3D Gaussian splatting. Efficient vision-based spatial representation poses a significant challenge due to the memory demands of visual information. Recent advances in 3D rendering technologies, such as neural radiance fields (NeRF) and 3D Gaussian splatting, have prompted exploration of their applications in robotics. However, such 3D rendering methods often focus on rendering high-quality images, requiring numerous parameters and resulting in large data, which are unsuitable for robotics applications. To tackle this challenge, we introduce 3DSR, an efficient voxelized renderable neural 3D spatial representation that utilizes 3D Gaussian splatting. 3DSR leverages the strengths of both voxelization (memory efficiency) and 3D Gaussian splatting (high-quality image reconstruction). The proposed method achieves memory efficiency by reducing the number of 3D Gaussians in the 3D representation through voxelization, while preserving the image quality required for effective vision-based robotic applications. Experimental results demonstrate that 3DSR achieves over 90% of the best method’s reconstruction quality while requiring only 54.54% of its memory. Additional experiments on visual localization and navigation further confirm that the proposed method is readily applicable to robotics.
  • Video