6 accepted papers
Large language models (LLMs) have emerged as the dominant paradigm for robotic task planning using natural language instructions. However, trained on general internet data, LLMs are not inherently aligned with the embodiment, skill sets, and limitations of real-world robotic systems. Inspired by the
Many real-world tasks, such as assembly, cooking, and object handovers, require bi-manual coordination. Learning such skills via imitation remains challenging due to dataset scarcity, mainly caused by the high cost of bi-manual robotic platforms and barriers to entry in robotics software. To address
This paper proposes a novel, more computationally efficient method for optimizing robot excitation trajectories for dynamic parameter identification, emphasizing self-collision avoidance. This addresses the system identification challenges for getting high-quality training data associated with co-ma…
Transseptal puncture (TSP) is a prerequisite for left atrial catheter ablation for atrial fibrillation, requiring access from the right side of the heart. It is a demanding procedural step associated with complications, including inadvertent puncturing and application of large forces on the tissue w…
This paper presents OpTaS, a task specification Python library for Trajectory Optimization (TO) and Model Predictive Control (MPC) in robotics. Both TO and MPC are increasingly receiving interest in optimal control and in particular handling dynamic environments. While a flurry of software libraries…
Performing a number of motion patterns — referred to as skills — (e.g.; wave; spiral; sweeping motions) during teleoperation is an integral part of many industrial processes such as spraying; welding; and wiping (cleaning; polishing). Maintaining these motions whilst simultaneously avoiding obstacle…