Varun Raveendra, Connor Mattson, Daniel S. Browncs.RO cs.AI
Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, little research explores how agents can infer swarm-level behavior from purely local perception, a capability critical for detecting faults and behavior changes. In this paper, we introduce Synchronized Swarm Behavior Classification (SyncSBC), which combines improvements in machine learning and distributed consensus to classify collective swarm behavior and synchronize swarm decision-making in an entirely decentralized manner. We show that SyncSBC achieves high classification accuracy and low synchronization delay, making it suitable for real-world deployment. Finally, we use SyncSBC to demonstrate two promising swarm applications on real robots where we show that swarms utilizing SyncSBC can accurately identify anomalies in robot behavior and autonomously coordinate collective changes in swarm behavior. Videos, code and supplemental experiments are available at https://sites.google.com/view/sync-sbc/home.
Autonomous exploration of unknown 3D environments is traditionally driven by coverage-maximizing geometric heuristics. However, these methods typically determine exploration targets without considering the underlying structural context. This leads to inefficient trajectories often limiting the fidelity of the final 3D reconstruction. To bridge the gap between spatial coverage and reconstruction quality, we introduce a novel paradigm: reframing exploration as a geometric anomaly minimization problem. We present SCAGE: SCene Anomaly Guided Exploration, a novel autonomous exploration framework that operates directly on unstructured 3D point clouds. Instead of blindly chasing volumetric boundaries, we equip the robot with a foundational understanding of standard indoor architecture. As the robot navigates, it continuously evaluates its live 3D observations against these learned expectations. When the incoming geometry contradicts the learned priors of a typical indoor environment, such as a fragmented wall or a partial table, the system flags these regions as scene anomalies. These geometric inconsistencies act as a guiding signal, naturally drawing the robot to investigate and resolve these structural anomalies from optimal vantage points. By actively targeting poorly reconstructed regions rather than just empty space, our approach seamlessly couples spatial discovery with high-fidelity mapping. Extensive evaluations demonstrate that SCAGE achieves superior volumetric coverage (~90% in all scenes) and higher 3D reconstruction quality compared to state-of-the-art baselines.
Under the Shipping 4.0 paradigm, autonomous and reduced-crew vessels require intelligent internal systems to maintain operational safety and structural stability. Ballast-water control is essential for ship trim and integrity, but conventional rule-based or manual approaches have limited adaptability to hydraulic anomalies such as valve failures and pipe blockages, and often depend on dense pressure or flow sensors for diagnosis. To address these limitations, this paper proposes RL-Ballast, a graph-based deep reinforcement learning framework for adaptive ballast-water path planning and sensor-frugal blockage candidate scoring. The valve-permutation problem is transformed into 54 feasible fluid-transfer routes generated using graph theory and depth-first search. The partially observable ballast environment is approximated with frame-stacked tank levels and action outcomes, allowing the agent to infer hidden blockage effects without explicitly modeling a high-dimensional POMDP. During deterministic inference, episode-level failed-action memory and dynamic action masking prevent repeated ineffective actions and support immediate rerouting. Failed transfer histories are further accumulated to rank suspicious valves or pipe segments without dense instrumentation. Monte Carlo simulations show that RL-Ballast completes all unexpected single-blockage scenarios and reduces average decision steps from 61.0 to 41.5 compared with a Dijkstra rule-based baseline. For diagnostic support, the failure-history scoring scheme achieves a 100% Top-3 hit rate, a 66.7% strict Top-1 hit rate, and an 83.3% Top-1 tie-hit rate under serially indistinguishable blockage conditions. These results suggest that RL-Ballast enables adaptive rerouting and maintenance-oriented blockage diagnosis under limited sensing conditions.
Philipp Allgeuer, Jan-Gerrit Habekost, Stefan Wermtercs.CV
Robots operating in real-world environments must in general be able to recognize previously unseen objects. As robotic systems move toward open-world autonomy, there is a growing, yet largely unmet, need for open vocabulary object detectors that are prompt-free and efficient enough for continuous deployment. We present AnomNOVIC, a two-stage known-workspace framework that combines a masked autoencoder (MAE) trained for anomaly detection, with NOVIC, a powerful real-time prompt-free open vocabulary image classifier. The MAE produces generic object-agnostic bounding boxes, allowing NOVIC to classify salient image regions without requiring a predefined candidate class list. We evaluate AnomNOVIC against strong open vocabulary baselines in a tabletop robot-object environment featuring the NICOL humanoid robot, reaching 47.1% AP / 57.5% AP50 for prompt-free recognition, and 59.0% AP / 72.5% AP50 if class candidates are provided. Across additional datasets, including an in-the-wild test set with 48 unique objects, AnomNOVIC reaches up to 82.6% prompt-free detection and classification accuracy. These results significantly surpass all tested open vocabulary baselines, including YOLO-World-v2, OWLv2, and YOLOE.
While autonomous rovers have become indispensable to precision farming, achieving consistent operational safety remains a critical challenge. Conventional safety sensors, such as LiDAR, fail to detect obstacles positioned below the plant canopy, posing a significant risk. While camera-based supervised learning methods can detect common objects, they perform poorly when faced with obstacles that were not present in their training data. Actual unsupervised anomaly detection offers a solution by learning the normal visual patterns of an environment, but often fails for the dynamic scenes captured by a moving rover.\\ This paper introduces Video Memory Transformers for Anomaly Detection (VMTAD), a fully unsupervised method designed for real-time obstacle detection in dynamic agricultural scenes. VMTAD utilizes a transformer-driven architecture augmented with a dedicated memory module. This memory module leverages temporal context by processing encoded representations of preceding frames. This approach enables the system to effectively address the dynamic context caused by the robot's movement. The model is trained using only images that represent normal operation, requiring no data labels.\\ VMTAD was rigorously evaluated on the 'Grillion' agricultural rover. On a challenging rapeseed dataset, VMTAD achieved state-of-the-art performance, reaching a 0.973 detection and 0.997 segmentation Area Under the Receiver Operating Characteristic curve. A lightweight variant provides an optimal balance of high accuracy and real-time inference (14 ms), which is critical for safety, as confirmed by our analysis of the rover's total stopping distance.
Identifying complex and safety-critical driving scenarios in large unlabelled datasets is an important but expensive problem. Existing approaches rely on human annotators, supervised classifiers, or carefully engineered rule sets, all of which require substantial prior knowledge about what constitutes a difficult scenario. We ask whether a model can discover scenario complexity on its own, with no labels at any stage. We train a minimal Joint Embedding Predictive Architecture (JEPA) on structured agent state data from the nuPlan mini dataset and use the temporal prediction error as a zero-shot complexity score. Without access to any ground-truth labels during training or evaluation setup, the model assigns significantly higher scores to scenarios involving unprotected turns, crosswalk interactions, and pedestrian proximity, and significantly lower scores to lane-following and stationary-traffic scenarios. We validate this finding through four ablation experiments that isolate the source of the signal, and through a downstream anomaly detection evaluation that achieves Average Precision of 0.512 against a 0.436 chance baseline. The results show that temporal prediction error in a self-supervised latent world model is a practical proxy for driving scenario complexity.