Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Fisac, Jaime F"'
Autor:
Bajcsy, Andrea, Fisac, Jaime F.
Artificial intelligence (AI) is interacting with people at an unprecedented scale, offering new avenues for immense positive impact, but also raising widespread concerns around the potential for individual and societal harm. Today, the predominant pa
Externí odkaz:
http://arxiv.org/abs/2405.09794
Despite the impressive recent advances in learning-based robot control, ensuring robustness to out-of-distribution conditions remains an open challenge. Safety filters can, in principle, keep arbitrary control policies from incurring catastrophic fai
Externí odkaz:
http://arxiv.org/abs/2405.00846
Autor:
Hu, Haimin, Dragotto, Gabriele, Zhang, Zixu, Liang, Kaiqu, Stellato, Bartolomeo, Fisac, Jaime F.
We consider the multi-agent spatial navigation problem of computing the socially optimal order of play, i.e., the sequence in which the agents commit to their decisions, and its associated equilibrium in an N-player Stackelberg trajectory game. We mo
Externí odkaz:
http://arxiv.org/abs/2402.09246
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot's ability to learn and adapt
Externí odkaz:
http://arxiv.org/abs/2309.01267
Safety is a central requirement for autonomous system operation across domains. Hamilton-Jacobi (HJ) reachability analysis can be used to construct "least-restrictive" safety filters that result in infrequent, but often extreme, control overrides. In
Externí odkaz:
http://arxiv.org/abs/2307.00193
The ability to accurately predict others' behavior is central to the safety and efficiency of interactive robotics. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as other agents' goals, attentio
Externí odkaz:
http://arxiv.org/abs/2302.00171
Autor:
Hu, Haimin, Fisac, Jaime F.
Publikováno v:
15th International Workshop on the Algorithmic Foundations of Robotics (WAFR) 2022
The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as people's goal
Externí odkaz:
http://arxiv.org/abs/2202.07720
Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using reinforcement learning often fail to generalize to novel environments due
Externí odkaz:
http://arxiv.org/abs/2201.08355
Reach-avoid optimal control problems, in which the system must reach certain goal conditions while staying clear of unacceptable failure modes, are central to safety and liveness assurance for autonomous robotic systems, but their exact solutions are
Externí odkaz:
http://arxiv.org/abs/2112.12288
Safety-critical applications require controllers/policies that can guarantee safety with high confidence. The control barrier function is a useful tool to guarantee safety if we have access to the ground-truth system dynamics. In practice, we have in
Externí odkaz:
http://arxiv.org/abs/2112.12210