Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reachability analysis approach to check in real-time that the system avoids dangerous regions of the state space. Specifically, we employ an optimization-based moving horizon estimator (MHE) to characterize the disturbance affecting the system, which is incorporated into an online reachability calculation. Reachable sets are calculated using a computational graph analysis tool to predict the possible future states of the system and verify that they satisfy safety constraints. We include theoretical arguments proving our approach generates reachable sets that bound the future states of the system, as well as numerical results demonstrating how it can be used for safety verification. Finally, we present results from hardware experiments demonstrating our approach’s ability to perform online reachability calculations for an unmanned surface vehicle subject to currents and actuator failures.

Planners using accurate models can be effective for accomplishing manipulation tasks in the real world, but are typically highly specialized and require significant fine-tuning to be reliable. Meanwhile, learning is useful for adaptation, but can require a substantial amount of data collection. In this paper, we propose a method that improves the efficiency of sub-optimal planners with approximate but simple and fast models by switching to a model-free policy when unexpected transitions are observed. Unlike previous work, our method specifically addresses when the planner fails due to transition model error by patching with a local policy only where needed. First, we use a sub-optimal model-based planner to perform a task until model failure is detected. Next, we learn a local model-free policy from expert demonstrations to complete the task in regions where the model failed. To show the efficacy of our method, we perform experiments with a shape insertion puzzle and compare our results to both pure planning and imitation learning approaches. We then apply our method to a door opening task. Our experiments demonstrate that our patch-enhanced planner performs more reliably than pure planning and with lower overall sample complexity than pure imitation learning.

We are developing a human–computer spoken language system capable of processing English, Cantonese and Putonghua (two dialects of Chinese). Such biliteracy and trilingualism characterize Hong Kong's language environment. Users can simply converse with the system to access real-time information in the foreign exchange domain. The system supports user calls with fixed-line as well as cellular phones, both of which have high penetration in Hong Kong. As such, we strive towards universal usability in developing speech and language interface technologies by (i) supporting multiple languages for user diversity; (ii) requiring no specialized training for system usage to avoid user knowledge gaps; and (iii) supporting device variety in telephone-based information access.

This paper reports on the design, development and evaluation of a bilingual telephone-based spoken language system for foreign exchange inquiries-the CU FOREX system. The telephone-based system supports Cantonese and English in a single spoken language interface to access real-time foreign exchange information. The specific domain covers information on the exchange rates between foreign currencies, as well as deposit interest rates of various time durations for a specified currency. Overall our system achieves a performance of 0.95 (kappa-statistic), with a typical transaction duration of 2 minutes on average.