This paper presents a novel semantics for the mA* epistemic action language that takes into consideration dynamic per-agent observability of events. Different from the original mA* semantics, the observability of events is defined locally at the level of possible worlds, giving a new method for compiling event models. Locally defined observability represents agents' uncertainty and false-beliefs about each others' ability to observe events. This allows for modeling second-order false-belief tasks where one agent does not know the truth about another agent's observations and resultant beliefs. The paper presents detailed constructions of event models for ontic, sensing, and truthful announcement action occurrences and proves various properties relating to agents' beliefs after the execution of an action. It also shows that the proposed approach can model second order false-belief tasks and satisfies the robustness and faithfulness criteria discussed by Bolander (2018, https://doi.org/10. 1007/978-3-319-62864-6_8).

We propose a novel approach to the problem of false belief revision in epistemic planning. Our state representations are pointed Kripke models with two binary relations over possible worlds: one representing agents' necessarily true knowledge, and one representing agents' possibly false beliefs. State transition functions maintain S5n properties in the knowledge relation and KD45n properties in the belief relation. When new information contradicts an agent's beliefs, belief revision draws new possible worlds from the agent's knowledge relation. Our method also improves upon prior work by accommodating false announcements. We develop our system as an extension to the mA* action language, presenting transition functions for ontic, sensing, and announcement actions.

Caterpillars are the soft bodied larvae of lepidopteran insects. They have evolved to occupy an extremely diverse range of natural environments and to locomote in complex three-dimensional structures without articulated joint or hydrostatic control. These animals make excellent bio-inspiration for the field of soft robotics because of their diversity and adaptability. In this paper, we present SquMA Bot, a caterpillar-inspired soft robot. The robot's body is primarily composed of a soft viscoelastic foam, and it is actuated using a motor-tendon system. SquMA Bot is able to mimic the inching gait of a caterpillar and can use its flexible body to adapt to a range of environments. This bio-inspired prototype demonstrates the effectiveness of a soft robot as a potential tool for exploring environments too dangerous for humans.

In some evolutionary robotics experiments, evolved robots are transferred from simulation to reality, while sensor/motor data flows back from reality to improve the next transferral. We envision a generalization of this approach: a simulation-to-reality pipeline. In this pipeline, increasingly embodied agents flow up through a sequence of increasingly physically realistic simulators, while data flows back down to improve the next transferral between neighboring simulators; physical reality is the last link in this chain. As a first proof of concept, we introduce a two-link chain: A fast yet low-fidelity ( lo-fi) simulator hosts minimally embodied agents, which gradually evolve controllers and morphologies to colonize a slow yet high-fidelity ( hi-fi) simulator. The agents are thus physically scaffolded. We show here that, given the same computational budget, these physically scaffolded robots reach higher performance in the hi-fi simulator than do robots that only evolve in the hi-fi simulator, but only for a sufficiently difficult task. These results suggest that a simulation-to-reality pipeline may strike a good balance between accelerating evolution in simulation while anchoring the results in reality, free the investigator from having to prespecify the robot's morphology, and pave the way to scalable, automated, robot-generating systems.