One important class of applications entails a robot scrutinizing, monitoring, or recording the evolution of an uncertain time-extended process. This sort of situation leads to an interesting family of active perception problems that can be cast as planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to. The distinguishing characteristic of this setting is that the robot has influence over what it captures via its sensors, but exercises no causal authority over the process evolving in the world. As such, the robot’s objective is to observe the underlying process and to produce a “chronicle” of occurrent events, subject to a goal specification of the sorts of event sequences that may be of interest. This paper examines variants of such problems in which the robot aims to collect sets of observations to meet a rich specification of their sequential structure. We study this class of problems by modeling a stochastic process via a variant of a hidden Markov model and specify the event sequences of interest as a regular language, developing a vocabulary of “mutators” that enable sophisticated requirements to be expressed. Under different suppositions on the information gleaned about the event model, we formulate and solve different planning problems. The core underlying idea is the construction of a product between the event model and a specification automaton. Using this product, we compute a policy that minimizes the expected number of steps to reach a goal state. We introduce a general algorithm for this problem as well as several more efficient algorithms for important special cases. The paper reports and compares performance metrics by drawing on some small case studies analyzed in depth via simulation. Specifically, we study the effect of the robot’s observation model on the average time required for the robot to record a desired story. We also compare our algorithm with a baseline greedy algorithm, showing that our algorithm outperforms the greedy algorithm in terms of the average time to record a desired story. In addition, experiments show that the algorithms tailored to specialized variants of the problem are rather more efficient than the general algorithm.
Conditioning Style on Substance: Plans for Narrative Observation
We consider a robot tasked with observing its environment and later selectively summarizing what it saw as a vivid, structured narrative. The robot interacts with an uncertain environment, modelled as a stochastic process, and must decide what events to pay attention to (substance), and how to best make its recording (style) for later compilation of its summary. If carrying a video camera, for example, it must decide where to be, what to aim the camera at, and which stylistic selections, like the focus and level of zoom, are most suitable. This paper examines planning algorithms that help the robot predict events that (1) will likely occur; (2) would be useful in telling a tale; and (3) may be hewed to cohere stylistically. The third factor, a time-extended requirement, is entirely neglected in earlier, simpler work. With formulations based on underlying Markov Decision Processes, we compare two algorithms: a monolithic planner that jointly plans over events and style pairs and a decoupled approach that prescribes style conditioned on events. The decoupled approach is seen to be effective and much faster to compute, suggesting that computational expediency justifies the separation of substance from style. Finally, we also report on our hardware implementation.
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- NSF-PAR ID:
- 10318014
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA48506.2021.9562095
