Efficient Causal Discovery for Robotics Applications

Using robots for automating tasks in environments shared with humans, such as warehouses, shopping centres, or hospitals, requires these robots to comprehend the fundamental physical interactions among nearby agents and objects. Specifically, creating models to represent cause-and-effect relationships among these elements can aid in predicting unforeseen human behaviours and anticipate the outcome of particular robot actions. To be suitable for robots, causal analysis must be both fast and accurate, meeting real-time demands and the limited computational resources typical in most robotics applications. In this paper, we present a practical demonstration of our approach for fast and accurate causal analysis, known as Filtered PCMCI (F-PCMCI), along with a real-world robotics application. The provided application illustrates how our F-PCMCI can accurately and promptly reconstruct the causal model of a human-robot interaction scenario, which can then be leveraged to enhance the quality of the interaction.Comment: Published at 5th Italian Conference on Robotics and Intelligent Machines (I-RIM 3D 2023

Environment-aware Interactive Movement Primitives for Object Reaching in Clutter

The majority of motion planning strategies developed over the literature for reaching an object in clutter are applied to two dimensional (2-d) space where the state space of the environment is constrained in one direction. Fewer works have been investigated to reach a target in 3-d cluttered space, and when so, they have limited performance when applied to complex cases. In this work, we propose a constrained multi-objective optimization framework (OptI-ProMP) to approach the problem of reaching a target in a compact clutter with a case study on soft fruits grown in clusters, leveraging the local optimisation-based planner CHOMP. OptI-ProMP features costs related to both static, dynamic and pushable objects in the target neighborhood, and it relies on probabilistic primitives for problem initialisation. We tested, in a simulated poly-tunnel, both ProMP-based planners from literature and the OptI-ProMP, on low (3-dofs) and high (7-dofs) dexterity robot body, respectively. Results show collision and pushing costs minimisation with 7-dofs robot kinematics, in addition to successful static obstacles avoidance and systematic drifting from the pushable objects center of mass

A Neuro-Symbolic Approach for Enhanced Human Motion Prediction

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy

From Continual Learning to Causal Discovery in Robotics

Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis encounters when applied to autonomous robots and how Continual Learning~(CL) could help to overcome them. We propose a possible way to leverage the CL paradigm to make causal discovery feasible for robotics applications where the computational resources are limited, while at the same time exploiting the robot as an active agent that helps to increase the quality of the reconstructed causal models

Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking

Robotic technology is increasingly considered the major mean for fruit picking. However, picking fruits in a dense cluster imposes a challenging research question in terms of motion/path planning as conventional planning approaches may not find collision-free movements for the robot to reach-and-pick a ripe fruit within a dense cluster. In such cases, the robot needs to safely push unripe fruits to reach a ripe one. Nonetheless, existing approaches to planning pushing movements in cluttered environments either are computationally expensive or only deal with 2-D cases and are not suitable for fruit picking, where it needs to compute 3- D pushing movements in a short time. In this work, we present a path planning algorithm for pushing occluding fruits to reach-and-pick a ripe one. Our proposed approach, called Interactive Probabilistic Movement Primitives (I-ProMP), is not computationally expensive (its computation time is in the order of 100 milliseconds) and is readily used for 3-D problems. We demonstrate the efficiency of our approach with pushing unripe strawberries in a simulated polytunnel. Our experimental results confirm I-ProMP successfully pushes table top grown strawberries and reaches a ripe one

Computed Torque Control for a VSA type Hybrid Shoulder Joint

Robotic anthropomorphism has been the study area of many researchers, which led to the conclusion that perceiving similar human features allow robots to be more socially accepted and integrated. Anthropomorphism can be thought from both morphological and functional point of views, while the degree of integration of both aspects determines the social impact of their union. One key aspect to the embodiment of functional anthropomorphism in robots is the impedance modulation. In this work, we present the first prototype of a variable stiffness actuated, hybrid type, robotic shoulder joint. We leverage our work on the anthropomorphic approach presented in our previous works for humanoids and humans applications. We then formulate the computed torque control scheme for such systems, controlled in fully autonomous scenarios

Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

From Human Perception and Action Recognition to Causal Understanding of Human-Robot Interaction in Industrial Environments

Human-robot collaboration is migrating from lightweight robots in laboratory environments to industrial applications, where heavy tasks and powerful robots are more common. In this scenario, a reliable perception of the humans involved in the process and related intentions and behaviors is fundamental. This paper presents two projects investigating the use of robots in relevant industrial scenarios, providing an overview of how industrial human-robot collaborative tasks can be successfully addressed

Survey of maps of dynamics for mobile robots

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area

THE DEVELOPMENT OF VARIABLE STIFFNESS SPHERICAL ARTIFICIAL JOINTS : AN APPROACH TO INCREASE ROBOT FUNCTIONAL ANTHROPOMORPHISM

Although robots need not necessarily be anthropomorphic in order to fulfill their function, humans are an unavoidable reference when dealing with unpredictable environments. Robots designed to share an environment with humans may not be absolutely accurate. Rather, concerns of paramount importance are dexterity and safety when dealing with scenarios involving physical interaction with humans. Examples of such scenarios include, but are not limited to, emergency services carried out by firefighters and paramedics during a natural disaster, prostheses use in amputees, and heavy equipment operations carried in industrial workplaces. If we look at human dynamics, we realize that nature usually adapts their behavior to ensure self safety and avoid environment damage by suitably modulating the mechanical impedance characteristics of their bodies. Embedding in robots human features such as softness, dynamics, and wide range of motion, can pave the way to unprecedented performance in these scenarios. My research work contributes to the state of the art of anthropomorphic robotic bodies in the direction of increasing structural and control anthropomorphism. The specific problem I tackle is the design and control of variable stiffness (VSA) multi-DoFs artificial joints, which can functionally replace human body articulations of the spherical type. Examples of articulations which can be roughly described as "spherical" in our bodies are at the neck, shoulders, hips, wrists and ankles. Indeed, current robotic and prosthetic technologies are well advanced in replicating rotoidal-type articulations (elbows, knees) also with VSA mechanisms that replicate the human ability to modulate mechanical impedance. However, at the state of the art, the design and control of effective spherical joints, especially with variable stiffness, is still lagging behind. In my work, I want to contribute in this direction. At first, I propose a 3 degrees of freedom (DoFs) hybrid spherical mechanism that combines characteristics from both serial and parallel kinematic chains, in order to replicate at the best the functionality and geometry of human biological joints. The joint configuration can be modulated to optimize the design of the joint in question. The proposed idea is implemented and validated on two prototypes, a 3-DoFs robotic neck and a 3-DoFs artificial shoulder joint that can be potentially applied in both robotic and prosthetic applications. Indeed, despite the separation in their origins, it is clear that humanoids and prostheses share the common ground of trying to replicate the dynamical behavior of human beings. I leverage the proposed solution on this fact to fill some gaps in both applications. From the aspect of human motor control, implementing a variable stiffness mechanism in spherical artificial joints can add functional anthropomorphism to the structural one. Indeed, the control inputs of an agonist-antagonist type VSA (prime movers angular positions) present analogy with those of the human muscle system (the threshold length). Hence, the agonist-antagonist type VSA can introduce the ability to closely reproduce the behavior of a pair of antagonistic muscles. The analogy can be obtained from a proper tuning of the mechanical system parameters. I propose first a control strategy that can map the estimation of the muscle activations, e.g. via ElectroMyoGraphic (EMG) sensors, on a variable stiffness elbow exoskeleton, FLExo. The latter is developed for assistive purposes in strenuous daily life activities (e.g in industrial and domestic frameworks). The control policy resulting from this mapping acts, in feed- forward, so as to exploit the muscle-like dynamics of the mechanical device. Thanks to the particular structure of the actuator, the joint stiffness naturally results from that mapping. I analyze first the linear and nonlinear structural properties of an accurate neuromuscular model augmented with an external assistive torque, then I build a simulation framework (Opensim/simulink interface) to have a benchmark for testing any controller structure on a neuromusculoskeletal-assistive device system. After that, I conduct experimental tests to prove the ability of the control policy to minimize human muscle effort to zero in assistive framework and under stability and robustness guarantees. Once validated, I propose a reformulation of the control strategy for use in a proof of concept 3-DoFs VSA spherical shoulder joint whose design is based on the hybrid kinematics that I study in the first part of my work. An inertial measurement unit/EMG interface is used to map the user desired joint position and stiffness, into the artificial joint. A better version of the VSA 3-DoFs artificial spherical joint is intended to be developed in future works and integrated into a 7-DoFs anthropomorphic VSA arm. Last but not least, a series elastic actuation version of the parallel joint is tested as an add-on for industrial manipulators to approach the problem of grasping in a box (highly constrained environment) while increasing their dexterity