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of 678
pro vyhledávání: '"TŮMOVÁ P."'
Control invariant sets play an important role in safety-critical control and find broad application in numerous fields such as obstacle avoidance for mobile robots. However, finding valid control invariant sets of dynamical systems under input limita
Externí odkaz:
http://arxiv.org/abs/2411.04833
Autor:
Kiessling, Alexander, Torroba, Ignacio, Sidrane, Chelsea Rose, Stenius, Ivan, Tumova, Jana, Folkesson, John
Informative path planning (IPP) applied to bathymetric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) m
Externí odkaz:
http://arxiv.org/abs/2410.15720
Autor:
Dong, Yifei, Han, Shaohang, Cheng, Xianyi, Friedl, Werner, Muchacho, Rafael I. Cabral, Roa, Máximo A., Tumova, Jana, Pokorny, Florian T.
Inherent robustness in manipulation is prevalent in biological systems and critical for robotic manipulation systems due to real-world uncertainties and disturbances. This robustness relies not only on robust control policies but also on the design c
Externí odkaz:
http://arxiv.org/abs/2409.11113
Autor:
Sidrane, Chelsea, Tumova, Jana
Reachable set computation is an important tool for analyzing control systems. Simulating a control system can show that the system is generally functioning as desired, but a formal tool like reachability analysis can provide a guarantee of correctnes
Externí odkaz:
http://arxiv.org/abs/2407.14394
Useful robot control algorithms should not only achieve performance objectives but also adhere to hard safety constraints. Control Barrier Functions (CBFs) have been developed to provably ensure system safety through forward invariance. However, they
Externí odkaz:
http://arxiv.org/abs/2407.12624
This work addresses maximally robust control synthesis under unknown disturbances. We consider a general nonlinear system, subject to a Signal Temporal Logic (STL) specification, and wish to jointly synthesize the maximal possible disturbance bounds
Externí odkaz:
http://arxiv.org/abs/2404.05535
One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases. This issue is further exacerbated if the problem considered is temporally dependent. State-of-the-art solutions today mainly follow cen
Externí odkaz:
http://arxiv.org/abs/2403.15916
In environments like offices, the duration of a robot's navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In thi
Externí odkaz:
http://arxiv.org/abs/2403.03727
Autor:
Sánchez, José Manuel Gaspar, Bruns, Leonard, Tumova, Jana, Jensfelt, Patric, Törngren, Martin
Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These c
Externí odkaz:
http://arxiv.org/abs/2401.06518
Partial observability and uncertainty are common problems in sequential decision-making that particularly impede the use of formal models such as Markov decision processes (MDPs). However, in practice, agents may be able to employ costly sensors to m
Externí odkaz:
http://arxiv.org/abs/2312.11227