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Learning-based controllers, such as diffusion policies and vision-language action models, often generate low-frequency or discontinuous robot state changes. Achieving smooth reference tracking requires a low-level controller that converts…
This paper proposes a unified robust motion controller for the position and force control problems of compliant robot manipulators driven by Series Elastic Actuators (SEAs). It is shown that the dynamic model of the compliant robot includes…
The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive robotics. We present a…
Robust implementable output regulator design approaches are studied for general linear continuous-time \mbox{systems} with periodically sampled measurements, consisting of both the regulation errors and extra measurements that are generally…
End-to-end engineering design pipelines, in which designs are evaluated using concurrently defined optimal controllers, are becoming increasingly common in practice. To discover designs that perform well even under the misspecification of…
Developing socially competent robots requires tight integration of robotics, computer vision, speech processing, and web technologies. We present the Socially-interactive Robot Software platform (SROS), an open-source framework addressing…
To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement…
Automated driving is currently a prominent area of scientific work. In the future, highly automated driving and new Advanced Driver Assistance Systems will become reality. While Advanced Driver Assistance Systems and automated driving…
Open-vocabulary mobile manipulation (OVMM) requires robots to follow language instructions, navigate, and manipulate while updating their world representation under dynamic environmental changes. However, most prior approaches update their…
Unsignalized intersections pose significant safety and efficiency challenges due to complex traffic flows. This paper proposes a novel roadside unit (RSU)-centric cooperative driving system leveraging global perception and…
The paper proposes a method for the correct by design coordination of autonomous driving systems (ADS). It builds on previous results on collision avoidance policies and the modeling of ADS by combining descriptions of their static…
Long-term autonomy requires robust navigation in environments subject to dynamic and static changes, as well as adverse weather conditions. Teach-and-Repeat (T\&R) navigation offers a reliable and cost-effective solution by avoiding the…
Robotic systems used in safety-critical industrial situations often rely on modular software architectures, and increasingly include autonomous components. Verifying that these modular robotic systems behave as expected requires approaches…
We present three dynamic error feedback controllers for robust output regulation of regular linear systems. These controllers are (i) a minimal order robust controller for exponentially stable systems (ii) an observer-based robust…
This paper presents the development of a fully autonomous delivery robot integrating mechanical engineering, embedded systems, and artificial intelligence. The platform employs a heterogeneous computing architecture, with RPi 5 and ROS 2…
In the last few years, researchers have applied machine learning strategies in the context of vehicular platoons to increase the safety and efficiency of cooperative transportation. Reinforcement Learning methods have been employed in the…
Safe autonomous driving in mixed traffic requires a unified understanding of multimodal interactions and dynamic planning under uncertainty. Existing learning based approaches struggle to capture rare but safety critical behaviors, while…
This paper considers the distributed robust suboptimal consensus control problem of linear multi-agent systems, with both H2 and H_infty performance requirements. A novel two-step complementary design approach is proposed. In the first…
We study in this paper the problem of adaptive trajectory tracking control for a class of nonlinear systems with parametric uncertainties. We propose to use a modular approach, where we first design a robust nonlinear state feedback which…
Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input. While LiDAR sensors provide reliably accurate information, existing end-to-end driving solutions are mainly…