The ubiquitous deployment of robots across diverse domains, from industrial automation to personal care, underscores their critical role in modern society. However, this growing dependence has also revealed security vulnerabilities. An attack vector involves the deployment of malicious software (malware) on robots, which can cause harm to robots themselves, users, and even the surrounding environment. Machine learning approaches, particularly supervised ones, have shown promise in malware detection by building intricate models to identify known malicious code patterns. However, these methods are inherently limited in detecting unseen or zero-day malware variants as they require regularly updated massive datasets that might be unavailable to robots. To address this challenge, we introduce RoboGuardZ, a novel malware detection framework based on zero-shot learning for robots. This approach allows RoboGuardZ to identify unseen malware by establishing relationships between known malicious code and benign behaviors, allowing detection even before the code executes on the robot. To ensure practical deployment in resource-constrained robotic hardware, we employ a unique parallel structured pruning and quantization strategy that compresses the RoboGuardZ detection model by 37.4% while maintaining its accuracy. This strategy reduces the size of the model and computational demands, making it suitable for real-world robotic systems. We evaluated RoboGuardZ on a recent dataset containing real-world binary executables from multi-sensor autonomous car controllers. The framework was deployed on two popular robot embedded hardware platforms. Our results demonstrate an average detection accuracy of 94.25% and a low false negative rate of 5.8% with a minimal latency of 20 ms, which demonstrates its effectiveness and practicality.

Zero-day vulnerabilities pose a significant challenge to robot cyber-physical systems (CPS). Attackers can exploit software vulnerabilities in widely-used robotics software, such as the Robot Operating System (ROS), to manipulate robot behavior, compromising both safety and operational effectiveness. The hidden nature of these vulnerabilities requires strong defense mechanisms to guarantee the safety and dependability of robotic systems. In this paper, we introduce RoboCop, a cyber-physical attack detection framework designed to protect robots from zero-day threats. RoboCop leverages static software features in the pre-execution analysis along with runtime state monitoring to identify attack patterns and deviations that signal attacks, thus ensuring the robot’s operational integrity. We evaluated RoboCop on the F1-tenth autonomous car platform. It achieves a 93% detection accuracy against a variety of zero-day attacks targeting sensors, actuators, and controller logic. Importantly, in on-robot deployments, it identifies attacks in less than 7 seconds with a 12% computational overhead.

The promise of individualized care for improving animal welfare demands real-time continuous monitoring of animals. While technology has helped crop agriculture realize the goals of precision care, animal agriculture is still lacking domain-adapted technology. In this work, we present a novel Criticality-Aware Self-Powered in-vivo sensing Edge for pRecision animal agriculture, CASPER. Enabling real-time monitoring of a suite of biomarkers while scavenging power from both thermal and physiological sources, CASPER promises unprecedented adaptability, range, and life-cycle for such an edge node. Field deployments show that CASPER generates 30mW of power with a surplus of 9.08mW, during the ultra-low power mode. The criticality-aware control is proven to capture deviation in trends of biomarker activity that would be missed in fixed-interval transmission. Hence proving the validity and effectiveness of CASPER.

Smart devices have entered all spheres of modern living, from monitoring the steps we walk to managing refrigerator inventory, ushering in the dawn of a new urban experience. The kitchen is the heart of the home; a place to share, care for and nurture the family unit, but also a place seeing the greatest impact from the introduction of smart devices. The smart sensing and remote control capability of smart appliances have enabled great physical convenience for users but have had less impact on cognitive conveniences. While such devices can sensewhatthey are working with, they fail to understandwhothey are working for, leaving much of the burden of trivial planning and decision making to humans with less personalized services. Hence, we introduce TupperwareEarth, a knowledge-based ontological semantic network for the “Internet of Kitchen Things” with the aim of reducing physical as well as cognitive loads of humans in cooking tasks. Also, we present a testbed for exploring kitchen innovation and validating the effectiveness of TupperwareEarth that combines intelligent kitchen storage containers, Smart Tupperware, and existing smart kitchen appliances through an Internet of Things network and a user-friendly front-end interface, Tuppy. Using this testbed, the quantitative user studies show a 33% reduction in average food preparation time and qualitative user surveys show that 75% of the users observed a significant reduction in cognitive loads, thereby validating the cognitive conveniences granted by TupperwareEarth.

Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand multimodal perception for effective and precise task execution. However, these present a novel challenge to robots as they lack the ability to combine these multimodal stimuli for performing contact-rich tasks. Learning-based methods have attempted to model multi-modal contact-rich tasks, but they often require extensive training examples and task-specific reward functions which limits their practicality and scope. Hence, we propose a generalizable model-free learning-from-demonstration framework for robots to learn contact-rich skills without explicit reward engineering. We present a novel multi-modal sensor data representation which improves the learning performance for contact-rich skills. We performed training and experiments using the real-life Sawyer robot for three everyday contact-rich skills – cleaning, writing, and peeling. Notably, the framework achieves a success rate of 100% for the peeling and writing skill, and 80% for the cleaning skill. Hence, this skill learning framework can be extended for learning other physical manipulation skills.

Telesurgery can be hindered by high-latency and low-bandwidth communication networks, often found in austere settings. Even delays of less than one second are known to negatively impact surgeries. To tackle the effects of connectivity associated with telerobotic surgeries, we propose the DESERTS framework. DESERTS provides a novel simulator interface where the surgeon can operate directly on a virtualized reality simulation and the activities are mirrored in a remote robot, almost simultaneously. Thus, the surgeon can perform the surgery uninterrupted, while high-level commands are extracted from his motions and are sent to a remote robotic agent. The simulated setup mirrors the remote environment, including an alpha-blended view of the remote scene. The framework abstracts the actions into atomic surgical maneuvers (surgemes) which eliminate the need to transmit compressed video information. This system uses a deep learning based architecture to perform live recognition of the surgemes executed by the operator. The robot then executes the received surgemes, thereby achieving semi-autonomy. The framework’s performance was tested on a peg transfer task. We evaluated the accuracy of the recognition and execution module independently as well as during live execution. Furthermore, we assessed the framework’s performance in the presence of increasing delays. Notably, the system maintained a task success rate of 87% from no-delays to 5 seconds of delay.