A survey of Semantic Reasoning frameworks for robotic systems

Liu, Weiyu; Daruna, Angel; Patel, Maithili; Ramachandruni, Kartik; Chernova, Sonia

doi:10.1016/j.robot.2022.104294

Cited by 8 publications

(1 citation statement)

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“…They are also used in interpretability, as their relational structure can be easily probed for causal effects on the robot's behaviour [164,42,41]. The challenge is that building comprehensive KGs is effortful, as the entities and relations must be defined, stored, managed, extended, and made by a human [160,104]. Furthermore, KGs can amass a large amount of entities and relationships, resulting in semantic redundancies (like synonyms) over a large search space that makes inference harder and slower [119].…”

Section: Graphical Structuresmentioning

confidence: 99%

Aligning Robot and Human Representations

Bobu¹,

Andi²,

Agrawal³

et al. 2023

Preprint

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To act in the world, robots rely on a representation of salient task aspects: for example, to carry a cup of coffee, a robot must consider movement efficiency and cup orientation in its behaviour. However, if we want robots to act for and with people, their representations must not be just functional but also reflective of what humans care about, i.e. their representations must be aligned with humans'. In this survey, we pose that current reward and imitation learning approaches suffer from representation misalignment, where the robot's learned representation does not capture the human's true representation. We suggest that because humans will be the ultimate evaluator of robot performance in the world, it is critical that we explicitly focus our efforts on aligning learned task representations with humans, in addition to learning the downstream task. We advocate that current representation learning approaches in robotics should be studied from the perspective of how well they accomplish the objective of representation alignment. To do so, we mathematically define the problem, identify its key desiderata, and situate current robot learning methods within this formalism. We conclude the survey by suggesting future directions for exploring open challenges.

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