Despite the remarkable acceleration of robotic development through advanced simulation technology, robotic applications are often subject to performance reductions in real-world deployment due to the inherent discrepancy between simulation and reality, often referred to as the "sim-to-real gap". This gap arises from factors like model inaccuracies, environmental variations, and unexpected disturbances. Similarly, model discrepancies caused by system degradation over time or minor changes in the system's configuration also hinder the effectiveness of the developed methodologies. Effectively closing these gaps is critical and remains an open challenge. This work proposes a lightweight conformal mapping framework to transfer control and planning policies from an expert teacher to a degraded less capable learner. The method leverages Schwarz-Christoffel Mapping (SCM) to geometrically map teacher control inputs into the learner's command space, ensuring maneuver consistency. To demonstrate its generality, the framework is applied to two representative types of control and planning methods in a path-tracking task: 1) a discretized motion primitives command transfer and 2) a continuous Model Predictive Control (MPC)-based command transfer. The proposed framework is validated through extensive simulations and real-world experiments, demonstrating its effectiveness in reducing the sim-to-real gap by closely transferring teacher commands to the learner robot.

Autonomous mobile robots (AMRs) equipped with high-quality cameras have revolutionized the field of inspections by providing efficient and cost-effective means of conducting surveys. The use of autonomous inspection is becoming more widespread in a variety of contexts, yet it is still challenging to acquire the best inspection information autonomously. In situations where objects may block a robot's view, it is necessary to use reasoning to determine the optimal points for collecting data. Although researchers have explored cloud-based applications to store inspection data, these applications may not operate optimally under network constraints, and parsing these datasets can be manually intensive. Instead, there is an emerging requirement for AMRs to autonomously capture the most informative views efficiently. To address this challenge, we present an autonomous Next-Best-View (NBV) framework that maximizes the inspection information while reducing the number of pictures needed during operations. The framework consists of a formalized evaluation metric using ray-tracing and Gaussian process interpolation to estimate information reward based on the current understanding of the partially-known environment. A derivative-free optimization (DFO) method is used to sample candidate views in the environment and identify the NBV point. The proposed approach's effectiveness is shown by comparing it with existing methods and further validated through simulations and experiments with various vehicles.

Thus, we introduce Multi-robot systems (MRS) have the potential to assist a coordinated epistemic prediction and planning method in many safety-critical applications such as search and rescue, in which a robot propagates a finite set of belief states military intelligence and surveillance, and inspection representing possible states of other agents in the system and operations where it may be hazardous and costly to deploy empathy states representing a finite set of possible states from humans. Looking to the state-of-the-art, we note that most other agents' perspectives. Subsequently, using epistemic MRS research assumes constant communication between planning, we can formulate a consensus strategy such that robots [1]-[3]. However, within the aforementioned application every distributed belief in the system achieves consensus. For space, long-range communication is often unreliable example, consider Figure 1 where two robots are canvassing or unavailable. Humans adequately cope with such problems, an environment. During disconnection, Robot 1 maintains a performing these tasks collaboratively by extrapolating and set of possible (belief) states for Robot 2 and also a set of empathizing with what other actors might believe if the local (empathy) states that Robot 2 might believe about Robot 1. plan must change at run-time. This subconscious process can Once Robot 2 experiences a failure, it tracks another state be modally represented as epistemic planning, computing in its empathy set. We reason that though Robot 1 holds a and reasoning about multiple predictions and actions while false belief about Robot 2's state, there exists an epistemic accounting for a priori beliefs, current observations, and strategy that can allow robot 1 to find robot 2 (i.e., updating other actors' sensing and mobility capabilities.