7 papers
PAC-DP: PAC-Bayesian Diffusion Policy Learning
Mohammad Hasan Yeganegi, Dian Yu, Andrea Del Prete +2
Diffusion Policies (DPs) are able to perform complex manipulation tasks. However, DPs are typically trained by minimizing a denoising objective, which provides limited control over…
PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control
Jiatao Ding, Songqun Gao, Andrea Del Prete +1
Reinforcement learning (RL) has emerged as a promising solution to accomplish complex robotic control tasks; however, most of the current work ignores the safety requirements. Safe…
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory Optimization
Elisa Alboni, Gianluigi Grandesso, Gastone Pietro Rosati Papini +2
Trajectory Optimization (TO) and Reinforcement Learning (RL) are powerful and complementary tools to solve optimal control problems. On the one hand, TO can efficiently compute loc…
CACTO-BIC: Scalable Actor-Critic Learning via Biased Sampling and GPU-Accelerated Trajectory Optimization
Elisa Alboni, Pietro Noah Crestaz, Elias Fontanari +1
Trajectory Optimization (TO) and Reinforcement Learning (RL) offer complementary strengths for solving optimal control problems. TO efficiently computes locally optimal solutions b…
Sample Efficient Certification of Discrete-Time Control Barrier Functions
Sampath Kumar Mulagaleti, Andrea Del Prete
Control Invariant (CI) sets are instrumental in certifying the safety of dynamical systems. Control Barrier Functions (CBFs) are effective tools to compute such sets, since the zer…
Parallel-Constraint Model Predictive Control: Exploiting Parallel Computation for Improving Safety
Elias Fontanari, Gianni Lunardi, Matteo Saveriano +1
Ensuring constraint satisfaction is a key requirement for safety-critical systems, which include most robotic platforms. For example, constraints can be used for modeling joint pos…