The development of robotics and AI agents has enabled their wider usage in human surroundings. AI agents are more trusted to make increasingly important decisions with potentially critical outcomes. It is essential to consider the ethical consequences of the decisions made by these systems. In this paper, we present how contrastive explanations can be used for comparing the ethics of plans. We build upon an existing ethical framework to allow users to make suggestions to plans and receive contrastive explanations.

Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.

This work addresses the problem of object delivery with a mobile robot in real-world environments. We introduce a multilayer, modular pushing skill that enables a robot to push unknown objects in such environments. We present a strategy that guarantees obstacle avoidance for object delivery by introducing the concept of a pushing corridor. This allows pushing objects in scattered and dynamic environments while exploiting the available collision-free area. Moreover, to push unknown objects, we propose an adaptive pushing controller that learns local inverse models of robot-object interaction on the fly. We performed exhaustive tests showing that our controller can adapt to various unknown objects with different mass and friction distributions. We show empirically that the proposed pushing skill leads towards successful pushes without prior knowledge and experience. The experimental results also demonstrate that the robot can successfully deliver objects in complex scenarios.

Pushing is a common task in robotic scenarios. In real-world environments, robots need to manipulate various unknown objects without previous experience. We propose a data-driven approach for learning local inverse models of robot-object interaction for push manipulation. The robot makes observations of the object behaviour on the fly and adapts its movement direction. The proposed model is probabilistic, and we update it using maximum a posteriori (MAP) estimation. We test our method by pushing objects with a holonomic mobile robot base. Validation of results over a diverse object set demonstrates a high degree of robustness and a high success rate in pushing objects towards a fixed target and along a path compared to previous methods. Moreover, based on learned inverse models, the robot can learn object properties and distinguish between different object behaviours when they are pushed from different sides.

Grasping in an uncertain environment is a topic of great interest in robotics. In this paper we focus on the challenge of object handover capable of coping with a wide range of different and unspecified objects. Handover is the action of object passing an object from one agent to another. In this work handover is performed from human to robot. We present a robust method that relies only on the force information from the wrist and does not use any vision and tactile information from the fingers. By analyzing readings from a wrist force sensor, models of tactile response for receiving and releasing an object were identified and tested during validation experiments.