STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning

Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections, and suboptimal behavior. Effective policy learning therefore requires frame-level advantages that distinguish reliable local progress from failures and regressions. We propose Self-supervised Temporal Ensemble Advantage Modeling (STEAM), a label-free method that learns such advantages from expert demonstrations. STEAM trains an ensemble of temporal-offset predictors on frame pairs within expert trajectories, using the normalized temporal offset between two frames as a self-supervised signal. Each predictor maps a frame pair to a distribution over temporal offsets, which is converted into a scalar advantage. STEAM then takes the minimum advantage across the ensemble to score mixed-quality rollout data conservatively. Across real-world bimanual towel folding, chip checkout, cola restocking, and single-arm pick-and-place tasks, STEAM identifies stalls, failures, and recoveries. When combined with CFGRL, STEAM further improves policy success rate by 59%, 54.3%, 23% and 16.2% over baselines, respectively.

ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation

3D scene graphs have empowered robots with semantic understanding for navigation and planning, yet they often lack the functional information required for physical manipulation, particularly regarding articulated objects. Existing approaches for inferring articulation mechanisms from static observations are prone to visual ambiguity, while methods that estimate parameters from state changes typically rely on constrained settings such as fixed cameras and unobstructed views. Furthermore, fine-grained functional elements like small handles are frequently missed by general object detectors. To bridge this gap, we present ArtiSG, a framework that constructs functional 3D scene graphs by encoding human demonstrations into structured robotic memory. Our approach leverages a robust articulation data collection pipeline utilizing a portable setup to accurately estimate 6-DoF articulation trajectories and axes even under camera ego-motion. We integrate these kinematic priors into a hierarchical and open-vocabulary graph while utilizing interaction data to discover inconspicuous functional elements missed by visual perception. Extensive real-world experiments demonstrate that ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision. Moreover, we show that the constructed graph serves as a reliable functional memory that effectively guides robots to perform language-directed manipulation tasks in real-world environments containing diverse articulated objects.

MR-COGraphs: Communication-efficient Multi-Robot Open-vocabulary Mapping System via 3D Scene Graphs

Collaborative perception in unknown environments is crucial for multi-robot systems. With the emergence of foundation models, robots can now not only perceive geometric information but also achieve open-vocabulary scene understanding. However, existing map representations that support open-vocabulary queries often involve large data volumes, which becomes a bottleneck for multi-robot transmission in communication-limited environments. To address this challenge, we develop a method to construct a graph-structured 3D representation called COGraph, where nodes represent objects with semantic features and edges capture their spatial relationships. Before transmission, a data-driven feature encoder is applied to compress the feature dimensions of the COGraph. Upon receiving COGraphs from other robots, the semantic features of each node are recovered using a decoder. We also propose a feature-based approach for place recognition and translation estimation, enabling the merging of local COGraphs into a unified global map. We validate our framework using simulation environments built on Isaac Sim and real-world datasets. The results demonstrate that, compared to transmitting semantic point clouds and 512-dimensional COGraphs, our framework can reduce the data volume by two orders of magnitude, without compromising mapping and query performance. For more details, please visit our website at https://github.com/efc-robot/MR-COGraphs.