Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain

Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobility research, showing that wheeled platforms have the potential of moving over vertically challenging terrain (e.g., rocky outcroppings, rugged boulders, and fallen tree trunks), invalidate both assumptions. Navigating off-road vehicle chassis with long suspension travel and low tire pressure in places where the boundary between obstacles and free spaces is blurry requires precise 3D modeling of the interaction between the chassis and the terrain, which is complicated by suspension and tire deformation, varying tire-terrain friction, vehicle weight distribution and momentum, etc. In this paper, we present a learning approach to model wheeled mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan feasible, stable, and efficient motion to drive over vertically challenging terrain without rolling over or getting stuck. We present physical experiments on two wheeled robots and show that planning using our learned model can achieve up to 60% improvement in navigation success rate and 46% reduction in unstable chassis roll and pitch angles.Comment: https://www.youtube.com/watch?v=VzpRoEZeyWk https://cs.gmu.edu/~xiao/Research/Verti-Wheelers

Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction

We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art

The Office of the Future: A Case Study on Workplace Effectiveness in the Biopharmaceutical Industry

Workplace strategies that incorporate office-of-the-future concepts enhance worker productivity and position organizations for radical changes to the ways work will be performed in the future. Due to the many challenges facing companies in the biopharmaceutical industry, many organizations now seek creative workplace improvements to support their continued success. As organizations assess how best to foster collaboration, knowledge sharing and productivity in the workplace, we are witnessing a seismic shift in this industry. A knowledge-based workforce, connected through globalization and empowered by technology, seeks optimal alignment in order to best maximize innovation. In this thesis, the contextual landscape of the biopharmaceutical industry is provided, the evolution of the workplace is examined, and a case of an office-of-the-future pilot program at a leading biopharmaceutical company is reported. Related elements, such as cost pressures, collaboration, organizational culture and environmental sustainability are interwoven into this paper to provide a multi-perspective analysis of the evolving ways in which work is performed. Companies who haven’t already adopted office-of-the-future concepts should soon examine their workplace strategy

REACTIVE MOTION REPLANNING FOR HUMAN-ROBOT COLLABORATION

Negli ultimi anni si è assistito a un incremento significativo di robot che condividono lo spazio di lavoro con operatori umani, per combinare la rapidità e la precisione proprie dei robot con l'adattabilità e l'intelligenza umana. Tuttavia, questa integrazione ha introdotto nuove sfide in termini di sicurezza ed efficienza della collaborazione. I robot devono essere in grado di adattarsi prontamente ai cambiamenti nell'ambiente circostante, come i movimenti degli operatori, adeguando in tempo reale il loro percorso per evitare collisioni, preferibilmente senza interruzioni. Inoltre, nelle operazioni di collaborazione tra uomo e robot, le traiettorie ripianificate devono rispettare i protocolli di sicurezza, al fine di evitare rallentamenti e fermate dovute alla prossimità eccessiva del robot all'operatore. In questo contesto è fondamentale fornire soluzioni di alta qualità in tempi rapidi per garantire la reattività del robot. Le tecniche di ripianificazione tradizionali tendono a faticare in ambienti complessi, soprattutto quando si tratta di robot con molti gradi di libertà e numerosi ostacoli di dimensioni considerevoli. La presente tesi affronta queste sfide proponendo un nuovo algoritmo sampling-based di ripianificazione del percorso per manipolatori robotici. Questo approccio sfrutta percorsi pre-calcolati per generare rapidamente nuove soluzioni in poche centinaia di millisecondi. Inoltre, incorpora una funzione di costo che guida l'algoritmo verso soluzioni che rispettano lo standard di sicurezza ISO/TS 15066, riducendo così gli interventi di sicurezza e promuovendo una cooperazione efficiente tra uomo e robot. Viene inoltre presentata un'architettura per gestire il processo di ripianificazione durante l'esecuzione del percorso del robot. Infine, viene introdotto uno strumento software che semplifica l'implementazione e il testing degli algoritmi di ripianificazione del percorso. Simulazioni ed esperimenti condotti su robot reali dimostrano le prestazioni superiori del metodo proposto rispetto ad altre tecniche popolari.In recent years, there has been a significant increase in robots sharing workspace with human operators, combining the speed and precision inherent to robots with human adaptability and intelligence. However, this integration has introduced new challenges in terms of safety and collaborative efficiency. Robots now need to swiftly adjust to dynamic changes in their environment, such as the movements of operators, altering their path in real-time to avoid collisions, ideally without any disruptions. Moreover, in human-robot collaborations, replanned trajectories should adhere to safety protocols, preventing safety-induced slowdowns or stops caused by the robot's proximity to the operator. In this context, quickly providing high-quality solutions is crucial for ensuring the robot's responsiveness. Conventional replanning techniques often fall short in complex environments, especially for robots with numerous degrees of freedom contending with sizable obstacles. This thesis tackles these challenges by introducing a novel sampling-based path replanning algorithm tailored for robotic manipulators. This approach exploits pre-computed paths to generate new solutions in a few hundred milliseconds. Additionally, it integrates a cost function that steers the algorithm towards solutions that strictly adhere to the ISO/TS 15066 safety standard, thereby minimizing the need for safety interventions and fostering efficient cooperation between humans and robots. Furthermore, an architecture for managing the replanning process during the execution of the robot's path is introduced. Finally, a software tool is presented to streamline the implementation and testing of path replanning algorithms. Simulations and experiments conducted on real robots demonstrate the superior performance of the proposed method compared to other popular techniques

A Study on Learning Social Robot Navigation with Multimodal Perception

Autonomous mobile robots need to perceive the environments with their onboard sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation decisions. In order to navigate human-inhabited public spaces, such a navigation task becomes more than only obstacle avoidance, but also requires considering surrounding humans and their intentions to somewhat change the navigation behavior in response to the underlying social norms, i.e., being socially compliant. Machine learning methods are shown to be effective in capturing those complex and subtle social interactions in a data-driven manner, without explicitly hand-crafting simplified models or cost functions. Considering multiple available sensor modalities and the efficiency of learning methods, this paper presents a comprehensive study on learning social robot navigation with multimodal perception using a large-scale real-world dataset. The study investigates social robot navigation decision making on both the global and local planning levels and contrasts unimodal and multimodal learning against a set of classical navigation approaches in different social scenarios, while also analyzing the training and generalizability performance from the learning perspective. We also conduct a human study on how learning with multimodal perception affects the perceived social compliance. The results show that multimodal learning has a clear advantage over unimodal learning in both dataset and human studies. We open-source our code for the community's future use to study multimodal perception for learning social robot navigation

Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning

Enabling autonomous robots to interact in unstructured environments with dynamic objects requires manipulation capabilities that can deal with clutter, changes, and objects' variability. This paper presents a comparison of different reinforcement learning-based approaches for object picking with a robotic manipulator. We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning and using a policy pre-trained on a task with a reduced action set to warm-start the full problem. For efficient and more flexible data collection, we train in simulation and transfer the policies to a real robot. We show that using curriculum learning, policies learned with a sparse reward formulation can be trained at similar rates as with a shaped reward. These policies result in success rates comparable to the policy initialized on the simplified task. We could successfully transfer these policies to the real robot with only minor modifications of the depth image filtering. We found that using a heuristic to warm-start the training was useful to enforce desired behavior, while the policies trained from scratch using a curriculum learned better to cope with unseen scenarios where objects are removed.Comment: 8 pages, video available at https://youtu.be/ii16Zejmf-

Learning of Parameters in Behavior Trees for Movement Skills

Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-performing policies, can produce dangerous behaviors while learning, and the optimized policies (usually modeled as neural networks) give almost zero explanation when they fail to perform the task. For these reasons, the adoption of RL in industrial settings is not common. Behavior Trees (BTs), on the other hand, can provide a policy representation that a) supports modular and composable skills, b) allows for easy interpretation of the robot actions, and c) provides an advantageous low-dimensional parameter space. In this paper, we present a novel algorithm that can learn the parameters of a BT policy in simulation and then generalize to the physical robot without any additional training. We leverage a physical simulator with a digital twin of our workstation, and optimize the relevant parameters with a black-box optimizer. We showcase the efficacy of our method with a 7-DOF KUKA-iiwa manipulator in a task that includes obstacle avoidance and a contact-rich insertion (peg-in-hole), in which our method outperforms the baselines.Comment: 8 pages, 5 figures, accepted at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS