相关论文: Interactive OT Gym: A Reinforcement Learning-Based…
Achieving both realism and controllability in closed-loop traffic simulation remains a key challenge in autonomous driving. Dataset-based methods reproduce realistic trajectories but suffer from covariate shift in closed-loop deployment,…
With the increasing use of assistive robots in rehabilitation and assisted mobility of human patients, there has been a need for a deeper understanding of human-robot interactions particularly through simulations, allowing an understanding…
Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong team coordination and effective OR management. Automatic…
Traditional telesurgery relies on the surgeon's full control of the robot on the patient's side, which tends to increase surgeon fatigue and may reduce the efficiency of the operation. This paper introduces a Robotic Partner (RP) to…
Modern commercial Heating, Ventilation, and Air Conditioning (HVAC) devices form a complex and interconnected thermodynamic system with the building and outside weather conditions, and current setpoint control policies are not fully…
Despite growing interest in Learning-by-Teaching (LbT), few studies have explored how this paradigm can be implemented with autonomous, peer-like social robots in real classrooms. Most prior work has relied on scripted or Wizard-of-Oz…
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient control design in presence of adversarial cyber-events, and…
Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, there are numerous challenges involved with the process of…
In hazardous and remote environments, robotic systems perform critical tasks demanding improved safety and efficiency. Among these, quadruped robots with manipulator arms offer mobility and versatility for complex operations. However,…
Soft growing robots are proposed for use in applications such as complex manipulation tasks or navigation in disaster scenarios. Safe interaction and ease of production promote the usage of this technology, but soft robots can be…
Intraocular foreign body removal demands millimeter-level precision in confined intraocular spaces, yet existing robotic systems predominantly rely on manual teleoperation with steep learning curves. To address the challenges of autonomous…
Large real-world robot datasets hold great potential to train generalist robot models, but scaling real-world human data collection is time-consuming and resource-intensive. Simulation has great potential in supplementing large-scale data,…
Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce…
Jumping poses a significant challenge for quadruped robots, despite being crucial for many operational scenarios. While optimisation methods exist for controlling such motions, they are often time-consuming and demand extensive knowledge of…
Objective: Enhancing the reliability of myoelectric controllers that decode motor intent is a pressing challenge in the field of bionic prosthetics. State-of-the-art research has mostly focused on Supervised Learning (SL) techniques to…
Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. Prior research has shown the possibility of learning tactile-guided policy from teleoperated demonstration data. However, to…
Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the reality gap). Some existing solutions to this sim-to-real problem, such as Grounded…
Current Reinforcement Learning (RL) algorithms struggle with long-horizon tasks where time can be wasted exploring dead ends and task progress may be easily reversed. We develop the SPOT framework, which explores within action safety zones,…
General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks. While reinforcement learning provides a powerful framework for acquiring individual behaviors, the time needed to…
Reinforcement learning (RL) has proven effective for AI-based building energy management. However, there is a lack of flexible framework to implement RL across various control problems in building energy management. To address this gap, we…