Learning Rock Pushability on Rough Planetary Terrain

In the context of mobile navigation in unstructured environments, the predominant approach entails the avoidance of obstacles. The prevailing path planning algorithms are contingent upon deviating from the intended path for an indefinite duration and returning to the closest point on the route after the obstacle is left behind spatially. However, avoiding an obstacle on a path that will be used repeatedly by multiple agents can hinder long-term efficiency and lead to a lasting reliance on an active path planning system. In this study, we propose an alternative approach to mobile navigation in unstructured environments by leveraging the manipulation capabilities of a robotic manipulator mounted on top of a mobile robot. Our proposed framework integrates exteroceptive and proprioceptive feedback to assess the push affordance of obstacles, facilitating their repositioning rather than avoidance. While our preliminary visual estimation takes into account the characteristics of both the obstacle and the surface it relies on, the push affordance estimation module exploits the force feedback obtained by interacting with the obstacle via a robotic manipulator as the guidance signal. The objective of our navigation approach is to enhance the efficiency of routes utilized by multiple agents over extended periods by reducing the overall time spent by a fleet in environments where autonomous infrastructure development is imperative, such as lunar or Martian surfaces.

Bidirectional Human Interactive AI Framework for Social Robot Navigation

Trustworthiness is a crucial concept in the context of human-robot interaction. Cooperative robots must be transparent regarding their decision-making process, especially when operating in a human-oriented environment. This paper presents a comprehensive end-to-end framework aimed at fostering trustworthy bidirectional human-robot interaction in collaborative environments for the social navigation of mobile robots. Our method enables a mobile robot to predict the trajectory of people and adjust its route in a socially-aware manner. In case of conflict between human and robot decisions, detected through visual examination, the route is dynamically modified based on human preference while verbal communication is maintained. We present our pipeline, framework design, and preliminary experiments that form the foundation of our proposition.

Multi-Object Graph Affordance Network: Enabling Goal-Oriented Planning through Compound Object Affordances

Learning object affordances is an effective tool in the field of robot learning. While the data-driven models delve into the exploration of affordances of single or paired objects, there is a notable gap in the investigation of affordances of compound objects that are composed of an arbitrary number of objects with complex shapes. In this study, we propose Multi-Object Graph Affordance Network (MOGAN) that models compound object affordances and predicts the effect of placing new objects on top of the existing compound. Given different tasks, such as building towers of specific heights or properties, we used a search based planning to find the sequence of stack actions with the objects of suitable affordances. We showed that our system was able to correctly model the affordances of very complex compound objects that include stacked spheres and cups, poles, and rings that enclose the poles. We demonstrated the applicability of our system in both simulated and real-world environments, comparing our systems with a baseline model to highlight its advantages.