Zobrazeno 1 - 10
of 361
pro vyhledávání: '"P, Fisac"'
While robust optimal control theory provides a rigorous framework to compute robot control policies that are provably safe, it struggles to scale to high-dimensional problems, leading to increased use of deep learning for tractable synthesis of robot
Externí odkaz:
http://arxiv.org/abs/2409.13867
Autor:
Hu, Haimin, DeCastro, Jonathan, Gopinath, Deepak, Rosman, Guy, Leonard, Naomi Ehrich, Fisac, Jaime Fernández
Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing, where an ego vehicle can choose to overtake the rival, or stay behind it until a safe overtaking "corridor" opens. While an expert human can do well at making su
Externí odkaz:
http://arxiv.org/abs/2406.09810
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:
Fisac, David, Liu, Mingkun
We classify closed curves on a once-punctured torus with a single self-intersection from a combinatorial perspective. We determine the number of closed curves with given word-length and with zero, one, and arbitrary self-intersections.
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Externí odkaz:
http://arxiv.org/abs/2404.09372
Generating realistic and controllable agent behaviors in traffic simulation is crucial for the development of autonomous vehicles. This problem is often formulated as imitation learning (IL) from real-world driving data by either directly predicting
Externí odkaz:
http://arxiv.org/abs/2404.02524
Autor:
Lidard, Justin, Hu, Haimin, Hancock, Asher, Zhang, Zixu, Contreras, Albert Gimó, Modi, Vikash, DeCastro, Jonathan, Gopinath, Deepak, Rosman, Guy, Leonard, Naomi Ehrich, Santos, María, Fisac, Jaime Fernández
As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motio
Externí odkaz:
http://arxiv.org/abs/2402.14174
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
Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions through proper grounding. However, LLM hallucination may result in robots confidentl
Externí odkaz:
http://arxiv.org/abs/2402.06529
We consider the problem of solving a family of parametric mixed-integer linear optimization problems where some entries in the input data change. We introduce the concept of cutting-plane layer (CPL), i.e., a differentiable cutting-plane generator ma
Externí odkaz:
http://arxiv.org/abs/2311.03350