Two papers from RLLAB are accepted to IROS 2026

[2026.06.17]

Learning Humanoid Loco-Manipulation with Responsibility-Induced Specialized Experts for Vision-Language-Action Models by Hogun Kee, Wooseok Oh, Jooyoung Kim, Jaeyeon Jeong, Hyewoo Jeong, Hyeondal Son, Ho Sung Lee, Minjae Kang, and Songhwai Oh

Abstract: In this paper, we present Responsibility-Induced Specialized Experts (RISE), a responsibility-driven multi-expert Vision–Language–Action (VLA) framework that learns long-horizon humanoid loco-manipulation from segmented locomotion and manipulation-oriented demonstrations. Humanoid loco-manipulation remains challenging due to (i) heterogeneous whole-body behaviors with substantially different action distributions and (ii) the difficulty of collecting reliable long-horizon demonstrations that include behavior transitions. RISE addresses these challenges by inducing behavior-specialized experts via responsibility-driven learning, where each expert’s responsibility reflects how well it reconstructs the target action. This mechanism encourages experts to specialize in different behavioral patterns and enables the policy to model heterogeneous action distributions. The resulting expert weighting dynamics allow RISE to coordinate experts over time and infer behavior transitions during execution, enabling long-horizon loco-manipulation despite being trained only on segmented data. We evaluate RISE in simulation and on a real humanoid robot, demonstrating robust loco-manipulation with frequent locomotion–manipulation transitions and improved performance over baselines trained on segmented demonstrations.

RVN-Bench: A Benchmark for Reactive Visual Navigation by Jaewon Lee, Jae Seok Heo, Gunmin Lee, Howoong Jun, Jeongwoo Oh, and Songhwai Oh

Abstract: Safe visual navigation is critical for indoor mobile robots operating in cluttered environments. Existing benchmarks, however, often neglect collisions or are designed for outdoor scenarios, making them unsuitable for indoor visual navigation. To address this limitation, we introduce the reactive visual navigation benchmark (RVN‑Bench), a collision-aware benchmark for indoor mobile robots. In RVN‑Bench, an agent must reach sequential goal positions in previously unseen environments using only visual observations and no prior map, while avoiding collisions. Built on the Habitat 2.0 simulator and leveraging high‑fidelity HM3D scenes, RVN‑Bench provides large‑scale, diverse indoor environments, defines a collision‑aware navigation task and evaluation metrics, and offers tools for standardized training and benchmarking. RVN‑Bench supports both online and offline learning by offering an environment for online reinforcement learning, a trajectory image dataset generator, and tools for producing negative trajectory image datasets that capture collision events. Experiments show that policies trained on RVN-Bench generalize effectively to unseen environments, demonstrating its value as a standardized benchmark for safe and robust visual navigation. We will release the RVN-Bench code to support further research.