Embodied reasoning systems integrate robotic hardware and cognitive processes to perform complex tasks, typically in response to a natural language query about a specific physical environment. This usually involves changing the belief about the scene or physically interacting and changing the scene (e.g. sort the objects from lightest to heaviest). In order to facilitate the development of such systems we introduce a new modular Closed Loop Interactive Embodied Reasoning (CLIER) approach that takes into account the measurements of non-visual object properties, changes in the scene caused by external disturbances as well as uncertain outcomes of robotic actions. CLIER performs multi-modal reasoning and action planning and generates a sequence of primitive actions that can be executed by a robot manipulator. Our method operates in a closed loop, responding to changes in the environment. Our approach is developed with the use of MuBle simulation environment and tested in$\mathbf{1 0}$interactive benchmark scenarios. We extensively evaluate our reasoning approach in simulation and in real-world manipulation tasks with a success rate above$\mathbf{7 6 \%}$and 64%, respectively.

In this work, we focus on unsupervised vision-language-action mapping in the area of robotic manipulation. Recently, multiple approaches employing pre-trained large language and vision models have been proposed for this task. However, they are computationally demanding and require careful fine-tuning of the produced output. A more lightweight alter-native would be the implementation of multimodal Variational Autoencoders (VAEs) which can extract the latent features of the data and integrate them into a joint representation, as has been demonstrated mostly on image-image or image-text data for the state-of-the-art models. Here, we explore whether and how multimodal VAEs can be employed in unsupervised robotic manipulation tasks in a simulated environment. Based on the results obtained, we propose a model-invariant training alternative that improves the models’ performance in a simulator by up to 55 %. Moreover, we systematically evaluate the challenges raised by individual tasks, such as object or robot position variability, number of distractors, or task length. Our work thus also sheds light on the potential benefits and limitations of using the current multimodal VAEs for unsupervised learning of robotic motion trajectories based on vision and language.

In haptic object discrimination, the effect of gripper embodiment, action parameters, and sensory channels has not been systematically studied. We used two anthropomorphic hands and two two-finger grippers to grasp two sets of deformable objects. On the object classification task, we found: 1) among classifiers, SVM on sensory features and LSTM on raw time series performed best across all grippers; 2) faster compression speeds degraded performance; 3) generalization to different grasping configurations was limited; transfer to different compression speeds worked well for the Barrett Hand only. Visualization of the feature spaces using PCA showed that gripper morphology and action parameters were the main source of variance, making generalization across embodiment or grip configurations very difficult. On the highly challenging dataset consisting of polyurethane foams alone, only the Barrett Hand achieved excellent performance. Tactile sensors can thus provide a key advantage even if recognition is based on stiffness rather than shape. The dataset with 24 000 measurements is publicly available.

Collaborative robots became a popular tool for increasing productivity in partly automated manufacturing plants. Intuitive robot teaching methods are required to quickly and flexibly adapt the robot programs to new tasks. Gestures have an essential role in human communication. However, in human-robot-interaction scenarios, gesture-based user interfaces are so far used rarely, and if they employ a one-to-one mapping of gestures to robot control variables. In this paper, we propose a method that infers the user's intent based on gesture episodes, the context of the situation, and common sense. The approach is evaluated in a simulated table-top manipulation setting. We conduct deterministic experiments with simulated users and show that the system can even handle the personal preferences of each user.

Human-Robot collaboration in home and industrial workspaces is on the rise. However, the communication between robots and humans is a bottleneck. Although people use a combination of different types of gestures to complement speech, only a few robotic systems utilize gestures for communication. In this paper, we propose a gesture pseudo-language and show how multiple types of gestures can be combined to express human intent to a robot (i.e., expressing both the desired action and its parameters - e.g., pointing to an object and showing that the object should be emptied into a bowl). The demonstrated gestures and the perceived tabletop scene (object poses detected by CosyPose) are processed in real-time) to extract the human's intent. We utilize behavior trees to generate reactive robot behavior that handles various possible states of the world (e.g., a drawer has to be opened before an object is placed into it) and recovers from errors (e.g., when the scene changes). Furthermore, our system enables switching between direct teleoperation of the end-effector and high-level operation using the proposed gesture sentences. The system is evaluated on increasingly complex tasks using a real 7-DoF Franka Emika Panda manipulator. Controlling the robot via action gestures lowered the execution time by up to 60%, compared to direct teleoperation.

In this paper, we present an integrated system that includes reasoning from visual and natural language inputs, action and motion planning, executing tasks by a robotic arm, manipulating objects, and discovering their properties. A vision to action module recognises the scene with objects and their attributes and analyses enquiries formulated in natural language. It performs multi-modal reasoning and generates a sequence of simple actions that can be executed by a robot. The scene model and action sequence are sent to a planning and execution module that generates a motion plan with collision avoidance, simulates the actions, and executes them. We use synthetic data to train various components of the system and test on a real robot to show the generalization capabilities. We focus on a tabletop scenario with objects that can be grasped by our embodied agent i.e. a 7DoF manipulator with a two-finger gripper. We evaluate the agent on 60 representative queries repeated 3 times (e.g., ’Check what is on the other side of the soda can’) concerning different objects and tasks in the scene. We perform experiments in a simulated and real environment and report the success rate for various components of the system. Our system achieves up to 80.6% success rate on challenging scenes and queries. We also analyse and discuss the challenges that such an intelligent embodied system faces.

The coordination of multiple robots operating simultaneously in the same workspace requires the integration of task allocation and motion scheduling. We focus on tasks in which the robot's actions are not confined to small volumes, but can also occupy a large time-varying portion of the workspace, such as in welding along a line. The optimization of such tasks presents a considerable challenge mainly due to the fact that different variants of task execution exist, for instance, there can be multiple starting points of lines or closed curves, differentfilling patterns of areas, etc. We propose a generic and computationally efficient optimization method which is based on constraint programming. It takes into account the kinematics of the robots and guarantees that the motions of the robots are collision-free while minimizing the overall makespan. We evaluate our approach on several use-cases of varying complexity: cutting, additive manufacturing, spot welding, inserting and tightening bolts, performed by a dual-arm robot. In terms of the makespan, the result is superior to task execution by one robot arm as well as by two arms not working simultaneously.