Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Akella, Prithvi"'
Autor:
Akella, Prithvi, Dixit, Anushri, Ahmadi, Mohamadreza, Lindemann, Lars, Chapman, Margaret P., Pappas, George J., Ames, Aaron D., Burdick, Joel W.
The need for a systematic approach to risk assessment has increased in recent years due to the ubiquity of autonomous systems that alter our day-to-day experiences and their need for safety, e.g., for self-driving vehicles, mobile service robots, and
Externí odkaz:
http://arxiv.org/abs/2403.18972
A robotic behavior model that can reliably generate behaviors from natural language inputs in real time would substantially expedite the adoption of industrial robots due to enhanced system flexibility. To facilitate these efforts, we construct a fra
Externí odkaz:
http://arxiv.org/abs/2309.14894
Autor:
Akella, Prithvi
The well-known quote by George Box states that "All models are wrong, but some are useful", and the controls and robotics communities alike have followed a similar paradigm to make significant theoretical and practical advances in the study of contro
Control Barrier Functions (CBFs) allow for efficient synthesis of controllers to maintain desired invariant properties of safety-critical systems. However, the problem of identifying a CBF remains an open question. As such, this paper provides a cons
Externí odkaz:
http://arxiv.org/abs/2304.03849
Autor:
Akella, Prithvi, Ames, Aaron D.
Efficient methods to provide sub-optimal solutions to non-convex optimization problems with knowledge of the solution's sub-optimality would facilitate the widespread application of nonlinear optimal control algorithms. To that end, leveraging recent
Externí odkaz:
http://arxiv.org/abs/2304.03739
Leveraging recent developments in black-box risk-aware verification, we provide three algorithms that generate probabilistic guarantees on (1) optimality of solutions, (2) recursive feasibility, and (3) maximum controller runtimes for general nonline
Externí odkaz:
http://arxiv.org/abs/2303.06258
Barrier-Based Test Synthesis for Safety-Critical Systems Subject to Timed Reach-Avoid Specifications
We propose an adversarial, time-varying test-synthesis procedure for safety-critical systems without requiring specific knowledge of the underlying controller steering the system. From a broader test and evaluation context, determination of difficult
Externí odkaz:
http://arxiv.org/abs/2301.09622
Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introdu
Externí odkaz:
http://arxiv.org/abs/2212.06253
Vanilla Reinforcement Learning (RL) can efficiently solve complex tasks but does not provide any guarantees on system behavior. To bridge this gap, we propose a three-step safe RL procedure for continuous action spaces that provides probabilistic gua
Externí odkaz:
http://arxiv.org/abs/2212.06129
The well-known quote from George Box states that: "All models are wrong, but some are useful." To develop more useful models, we quantify the inaccuracy with which a given model represents a system of interest, so that we may leverage this quantity t
Externí odkaz:
http://arxiv.org/abs/2209.09337