Zobrazeno 1 - 10
of 531
pro vyhledávání: '"Ames, Aaron"'
The deployment of robotic systems in real world environments requires the ability to quickly produce paths through cluttered, non-convex spaces. These planned trajectories must be both kinematically feasible (i.e., collision free) and dynamically fea
Externí odkaz:
http://arxiv.org/abs/2411.13507
Autor:
Csomay-Shanklin, Noel, Ames, Aaron D.
Control architectures are often implemented in a layered fashion, combining independently designed blocks to achieve complex tasks. Providing guarantees for such hierarchical frameworks requires considering the capabilities and limitations of each la
Externí odkaz:
http://arxiv.org/abs/2411.13506
Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framework for grasping with formal safety guarantees based on Control Barrier
Externí odkaz:
http://arxiv.org/abs/2411.07833
Autor:
Lum, Tyler Ga Wei, Li, Albert H., Culbertson, Preston, Srinivasan, Krishnan, Ames, Aaron D., Schwager, Mac, Bohg, Jeannette
This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative models for multi-finger grasping at scale, reliable real-world dexterous g
Externí odkaz:
http://arxiv.org/abs/2410.23701
Autonomous systems typically leverage layered control architectures with a combination of discrete and continuous models operating at different timescales. As a result, layered systems form a new class of hybrid systems composed of systems operating
Externí odkaz:
http://arxiv.org/abs/2409.14902
Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (RL), one popular approach has been to use massively-parallelized, domai
Externí odkaz:
http://arxiv.org/abs/2409.14562
Autor:
Olkin, Zachary, Ames, Aaron D.
Model Predictive Control (MPC) is a common tool for the control of nonlinear, real-world systems, such as legged robots. However, solving MPC quickly enough to enable its use in real-time is often challenging. One common solution is given by real-tim
Externí odkaz:
http://arxiv.org/abs/2409.12366
This paper proposes a safety-critical locomotion control framework employed for legged robots exploring through infeasible path in obstacle-rich environments. Our research focus is on achieving safe and robust locomotion where robots confront unavoid
Externí odkaz:
http://arxiv.org/abs/2409.10274
We present a hierarchical architecture to improve the efficiency of event-triggered control (ETC) in reducing resource consumption. This paper considers event-triggered systems generally as an impulsive control system in which the objective is to min
Externí odkaz:
http://arxiv.org/abs/2409.09812
Cyber-physical systems can be subject to sensor attacks, e.g., sensor spoofing, leading to unsafe behaviors. This paper addresses this problem in the context of linear systems when an omniscient attacker can spoof several system sensors at will. In t
Externí odkaz:
http://arxiv.org/abs/2409.08413