Work towards PhD degree under the supervision of Assoc. Prof. Vadim Indelman
When: 21.12.2020 at 13:30
Where: zoom
Abstract: Real life scenarios in Autonomous Systems (AS) and Artificial Intelligence (AI) involve agent(s) that are expected to reliably and efficiently operate online under different sources of uncertainty, often with limited knowledge regarding the environment. These settings necessitate probabilistic reasoning regarding high dimensional problem-specific states. Attaining such levels of autonomy involves two key processes, inference and decision making under uncertainty. The former maintains a belief regarding the high-dimensional state given available information thus far, while the latter, also often referred to as belief space planning (BSP), is entrusted with determining the next best action(s). However, as these problems are computationally expensive, simplifying assumptions or process streamlining are required in order to provide with online or real-time performance. In recent years the similarities between inference and control triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of the aforementioned efforts, inference and control, as well as inference and belief space planning are still treated as two separate processes.
We present in this talk the “Joint Inference and Belief Space Planning” (JIP), a novel paradigm that fully utilizes the similarities between probabilistic inference and BSP, thus enabling to re-use computationally expensive calculations. Through the symbiotic relation enabled by JIP we developed new approaches for inference – Ru-Use Belief Inference (RUBI), and for decision making under uncertainty – Incremental eXpectation BSP (iX-BSP). In RUBI we update inference with a precursory planning stage which can be considered as a deviation from conventional Bayesian inference.
Rather than updating the belief from the previous time instant with new incoming information (e.g.~measurements), RUBI exploits the fact that similar calculations are already performed within planning in order to appropriately update the belief in inference far more efficiently while preserving accuracy. The iX-BSP approach exploits calculations performed as part of previous planning sessions to efficiently solve a new planning session while accounting for the data that became available since then, which is particularly important while operating in uncertain, potentially dynamically changing, environments.
We demonstrate our novel paradigms on both simulation and real-world data considering active visual SLAM experiments, while benchmarking it against the current top of the line. We show that our paradigms save valuable computation time without introducing simplifying assumptions or affecting accuracy, thus bringing these advanced capabilities more feasible for an online setting.
You can see the seminar here