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Long-horizon tasks in unstructured environments are notoriously challenging for robots because they require the prediction of extensive action plans with thousands of steps while adapting to ever-changing conditions by reasoning among multimodal sensing spaces. Humans can efficiently tackle such compound problems by breaking them down into easily reachable abstract sub-goals, significantly reducing complexity. Inspired by this ability, we explore how we can enable robots to acquire sub-goal formulation skills for long-horizon tasks and generalize them to novel situations and environments. To address these challenges, we propose the Zero-shot Abstract Sub-goal Framework (ZAS-F), which empowers robots to decompose overarching action plans into transferable abstract sub-goals, thereby providing zero-shot capability in new task conditions. ZAS-F is an imitation-learning-based method that efficiently learns a task policy from a few demonstrations. The learned policy extracts abstract features from multimodal and extensive temporal observations and subsequently uses these features to predict task-agnostic sub-goals by reasoning about their latent relations. We evaluated ZAS-F in radio frequency identification (RFID) inventory tasks across various dynamic environments, a typical long-horizon task requiring robots to handle unpredictable conditions, including unseen objects and structural layouts. Ourexperiments demonstrated that ZAS-F achieves a learning efficiency 30 times higher than previous methods, requiring only 8k demonstrations. Compared to prior approaches, ZAS-F achieves a 98.3% scanning accuracy while significantly reducing the training data requirement. Further, ZAS-F demonstrated strong generalization, maintaining a scan success rate of 99.4% in real-world deployment without additional finetuning. In long-term operations spanning 100 rooms, ZAS-F maintained consistent performance compared to short-term tasks, highlighting its robustness against compounding errors. These results establish ZAS-F as an efficient and adaptable solution for long-horizon robotic tasks in unstructured environments. 

Dense RFID environments pose critical challenges such as Reader-to-Reader Interference (RRI), Reader-to-Tag Collisions (RTC), and inefficient resource utilization, which degrade system performance and scalability. Traditional Media Access Control (MAC) protocols, including CSMA and TDMA, struggle to address these issues effectively, particularly in dynamic and large-scale deployments. This paper introduces MCSMARA (Markov Decision Process (MDP)-based Carrier Sense Multiple Access with Reader Arbitration), a novel MAC protocol designed to optimize reader coordination in dense RFID networks. By leveraging an MDP framework, MCSMARA models reader state transitions and employs a utility-based arbitration mechanism to dynamically allocate frequencies and time slots. The protocol incorporates adaptive backoff and decentralized neighborhood discovery for efficient resource management without centralized control. Simulation results demonstrate that MCSMARA reduces collisions by up to 30%, improves throughput by 25%, and ensures superior scalability, supporting a large amount of readers with minimal computational overhead. These findings establish MCSMARA as a transformative solution for RFID networks in logistics, retail, and industrial IoT, with potential for extension to mobile and heterogeneous environments. 

Symmetry in mixed quantum states can manifest in two distinct forms: , where each individual pure state in the quantum ensemble is symmetric with the same charge, and , which applies only to the entire ensemble. This paper explores a novel type of spontaneous symmetry breaking (SSB) where a strong symmetry is broken to a weak one. While the SSB of a weak symmetry is measured by the long-ranged two-point correlation function, the strong-to-weak SSB (SWSSB) is measured by the . We prove that SWSSB is a universal property of mixed-state quantum phases, in the sense that the phenomenon of SWSSB is robust against symmetric low-depth local quantum channels. We also show that the symmetry breaking is “spontaneous” in the sense that the effect of a local symmetry-breaking measurement cannot be recovered locally. We argue that a thermal state at a nonzero temperature in the canonical ensemble (with fixed symmetry charge) should have spontaneously broken strong symmetry. Additionally, we study nonthermal scenarios where decoherence induces SWSSB, leading to phase transitions described by classical statistical models with bond randomness. In particular, the SWSSB transition of a decohered Ising model can be viewed as the “ungauged” version of the celebrated toric-code decodability transition. We confirm that, in the decohered Ising model, the SWSSB transition defined by the fidelity correlator is the only physical transition in terms of channel recoverability. We also comment on other (inequivalent) definitions of SWSSB, through correlation functions with higher Rényi indices. Published by the American Physical Society2025