Challenging manipulation tasks can be solved effectively by combining individual robot skills, which must be parameterized for the concrete physical environment and task at hand. This is time-consuming and difficult for human programmers, particularly for force-controlled skills. To this end, we present Shadow Program Inversion (SPI), a novel approach to infer optimal skill parameters directly from data. SPI leverages unsupervised learning to train an auxiliary differentiable program representation ("shadow program") and realizes parameter inference via gradient-based model inversion. Our method enables the use of efficient first-order optimizers to infer optimal parameters for originally non-differentiable skills, including many skill variants currently used in production. SPI zero-shot generalizes across task objectives, meaning that shadow programs do not need to be retrained to infer parameters for different task variants. We evaluate our methods on three different robots and skill frameworks in industrial and household scenarios. Code and examples are available at https://innolab.artiminds.com/icra2021.

As robots start to execute complex manipulation tasks, they are expected to improve their skill set over time as humans do. A prominent approach to accomplish this is having robots to keep models of their actions based on their experiences in order to improve their action executions in the future. In this paper, we present such a methodology where robots start to execute some actions with random parameters and record their generic execution logs with semantic annotations in a symbolic knowledge base for robots. Using the data inside logs, multivariate Gaussian mixture models are fitted to the high-level action parameters for later executions. These affordance models are being updated whenever a new execution is carried on. In essence, robots can use these continuously-updated probabilistic model for improving their actions To prove the applicability we demonstrate opening-a-fridge-door experiments with a PR2 robot.

In this paper we present KnowRob2, a second generation knowledge representation and reasoning framework for robotic agents. KnowRob2 is an extension and partial redesign of KnowRob, currently one of the most advanced knowledge processing systems for robots that has enabled them to successfully perform complex manipulation tasks such as making pizza, conducting chemical experiments, and setting tables. The knowledge base appears to be a conventional first-order time interval logic knowledge base, but it exists to a large part only virtually: many logical expressions are constructed on demand from data structures of the control program, computed through robotics algorithms including ones for motion planning and solving inverse kinematics problems, and log data stored in noSQL databases. Novel features and extensions of KnowRob2 substantially increase the capabilities of robotic agents of acquiring open-ended manipulation skills and competence, reasoning about how to perform manipulation actions more realistically, and acquiring commonsense knowledge.

To enable robots to perform human-level tasks flexibly in varying conditions, we need a mechanism that allows them to exchange knowledge between themselves for crowd-sourcing the knowledge gap problem. One approach to achieve this is to equip a cloud application with a range of encyclopedic knowledge (i.e. ontologies) and execution logs of different robots performing the same tasks in different environments. In this paper, we show how knowledge exchange between robots can be done using OPENEASE as the cloud application. We equipped OPENEASE with ontologies about the kitchen domain, execution logs of three robots operating in two different kitchens, and semantic descriptions of both environments. By addressing two different use cases, we show that two PR2 robots and one Fetch robot can successfully adapt each other's plan parameters and sub symbolic data to the experiments that they are conducting.

AI knowledge representation and reasoning methods consider actions to be blackboxes that abstract away from how they are executed. This abstract view does not suffice for the decision making capabilities required by robotic agents that are to accomplish manipulation tasks. Such robots have to reason about how to pour without spilling, where to grasp a pot, how to open different containers, and so on. To enable such reasoning it is necessary to consider how objects are perceived, how motions can be executed and parameterized, and how motion parameterization affects the physical effects of actions. To this end, we propose to complement and extend symbolic reasoning methods with KnowRobSIM, an additional reasoning infrastructure based on modern game engine technology, including the subsymbolic world modeling through data structures, action simulation based on physics engine, and world scene rendering. We demonstrate how KnowRobSIMcan perform powerful reasoning, prediction, and learning tasks that are required for informed decision making in object manipulation.

With the advancements in robotic technology and the progress in human-robot interaction research, the interest in deploying mixed human-robot teams in rescue missions is increasing. Due to their complementary capabilities in terms of locomotion, visibility and reachability of areas, human-robot teams are considerably deployed in real-world settings, albeit the robotic agents in such scenarios are normally fully teleoperated. A major barrier to successful and efficient mission execution in those teams is the lack of cognitive skills in robotic systems. In this paper, we present a cognition-enabled framework and an implemented system where robotic agents are equipped with cognitive capabilities to naturally communicate with humans and autonomously perform tasks. The framework allows for natural tasking of robots, reasoning about robot behavior, capabilities and actions, and a common belief state representation for shared mission awareness of robots and human operators.

Robotic agents that do everyday manipulation tasks can hugely benefit from being able to predict consequences of their actions just before the execution. However, such a simulation technique is usually computationally-expensive and may not be achieved with agents' self computing power. For this problem, cloud robotics may offer a solution. Cloud robotics is an emerging field in the intersection of robotics and cloud computing which enables robots to access a greater amount of processing power and storage capacity than it can employ within itself. In this work, we introduce a mental simulation service to, one of the cloud engines, openEASE [1]. Using this service, researchers and robots can describe the world model, the state of the agent and the problem that is being dealt with. In return, it simulates the world and runs a learning algorithm and suggests a solution how the robotic agent can handle the problem. This service does not only offer a free remote access to simulation which is computationally expensive but also thanks to OPEnEASE's rich reasoning techniques these simulated experiments can be reasoned later on using prolog queries.

In this paper we discuss how the combination of modern technologies in “big data” storage and management, knowledge representation and processing, cloud-based computation, and web technology can help the robotics community to establish and strengthen an open research discipline. We describe how we made the demonstrator of a EU project review openly available to the research community. Specifically, we recorded episodic memories with rich semantic annotations during a pizza preparation experiment in autonomous robot manipulation. Afterwards, we released them as an open knowledge base using the cloud- and web-based robot knowledge service OPENEASE. We discuss several ways on how this open data can be used to validate our experimental reports and to tackle novel challenging research problems.