Zobrazeno 1 - 10
of 3 181
pro vyhledávání: '"DOLAN, JOHN"'
Autor:
Sue, Guo Ning, Choudhary, Yogita, Desatnik, Richard, Majidi, Carmel, Dolan, John, Shi, Guanya
Ensuring safety via safety filters in real-world robotics presents significant challenges, particularly when the system dynamics is complex or unavailable. To handle this issue, learning-based safety filters recently gained popularity, which can be c
Externí odkaz:
http://arxiv.org/abs/2411.19809
Reinforcement learning (RL) agents need to explore their environment to learn optimal behaviors and achieve maximum rewards. However, exploration can be risky when training RL directly on real systems, while simulation-based training introduces the t
Externí odkaz:
http://arxiv.org/abs/2410.06570
Modern non-linear model-based controllers require an accurate physics model and model parameters to be able to control mobile robots at their limits. Also, due to surface slipping at high speeds, the friction parameters may continually change (like t
Externí odkaz:
http://arxiv.org/abs/2410.06565
Recent works in the robot learning community have successfully introduced generalist models capable of controlling various robot embodiments across a wide range of tasks, such as navigation and locomotion. However, achieving agile control, which push
Externí odkaz:
http://arxiv.org/abs/2409.15783
Autor:
Zhu, Zehang, Wang, Yuning, Ke, Tianqi, Han, Zeyu, Xu, Shaobing, Xu, Qing, Dolan, John M., Wang, Jianqiang
Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used beca
Externí odkaz:
http://arxiv.org/abs/2409.14688
Autor:
Wang, Yuning, Ke, Zehong, Jiang, Yanbo, Li, Jinhao, Xu, Shaobing, Dolan, John M., Wang, Jianqiang
Autonomous driving decision-making is one of the critical modules towards intelligent transportation systems, and how to evaluate the driving performance comprehensively and precisely is a crucial challenge. A biased evaluation misleads and hinders d
Externí odkaz:
http://arxiv.org/abs/2409.14680
This paper presents a system for enabling real-time synthesis of whole-body locomotion and manipulation policies for real-world legged robots. Motivated by recent advancements in robot simulation, we leverage the efficient parallelization capabilitie
Externí odkaz:
http://arxiv.org/abs/2409.10469
Varying dynamics pose a fundamental difficulty when deploying safe control laws in the real world. Safety Index Synthesis (SIS) deeply relies on the system dynamics and once the dynamics change, the previously synthesized safety index becomes invalid
Externí odkaz:
http://arxiv.org/abs/2409.09882
This paper proposes a novel learning-based framework for autonomous driving based on the concept of maximal safety probability. Efficient learning requires rewards that are informative of desirable/undesirable states, but such rewards are challenging
Externí odkaz:
http://arxiv.org/abs/2409.03160
This paper presents a novel approach to modeling human driving behavior, designed for use in evaluating autonomous vehicle control systems in a simulation environments. Our methodology leverages a hierarchical forward-looking, risk-aware estimation f
Externí odkaz:
http://arxiv.org/abs/2405.06578