Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Kang, Shucheng"'
We propose semidefinite trajectory optimization (STROM), a framework that computes fast and certifiably optimal solutions for nonconvex trajectory optimization problems defined by polynomial objectives and constraints. STROM employs sparse second-ord
Externí odkaz:
http://arxiv.org/abs/2406.05846
This paper considers sparse polynomial optimization with unbounded sets. When the problem possesses correlative sparsity, we propose a sparse homogenized Moment-SOS hierarchy with perturbations to solve it. The new hierarchy introduces one extra auxi
Externí odkaz:
http://arxiv.org/abs/2401.15837
Safety is critical in robotic tasks. Energy function based methods have been introduced to address the problem. To ensure safety in the presence of control limits, we need to design an energy function that results in persistently feasible safe contro
Externí odkaz:
http://arxiv.org/abs/2303.10277
We study the problem of verification and synthesis of robust control barrier functions (CBF) for control-affine polynomial systems with bounded additive uncertainty and convex polynomial constraints on the control. We first formulate robust CBF verif
Externí odkaz:
http://arxiv.org/abs/2303.10081
Model mismatches prevail in real-world applications. Ensuring safety for systems with uncertain dynamic models is critical. However, existing robust safe controllers may not be realizable when control limits exist. And existing methods use loose over
Externí odkaz:
http://arxiv.org/abs/2209.06896
Reinforcement learning shows great potential to solve complex contact-rich robot manipulation tasks. However, the safety of using RL in the real world is a crucial problem, since unexpected dangerous collisions might happen when the RL policy is impe
Externí odkaz:
http://arxiv.org/abs/2207.13438