POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints

In this paper, we seek to learn a robot policy guaranteed to satisfy state constraints. To encourage constraint satisfaction, existing RL algorithms typically rely on Constrained Markov Decision Processes and discourage constraint violations through reward shaping. However, such soft constraints cannot offer verifiable safety guarantees. To address this gap, we propose POLICEd RL, a novel RL algorithm explicitly designed to enforce affine hard constraints in closed-loop with a black-box environment. Our key insight is to force the learned policy to be affine around the unsafe set and use this affine region as a repulsive buffer to prevent trajectories from violating the constraint. We prove that such policies exist and guarantee constraint satisfaction. Our proposed framework is applicable to both systems with continuous and discrete state and action spaces and is agnostic to the choice of the RL training algorithm. Our results demonstrate the capacity of POLICEd RL to enforce hard constraints in robotic tasks while significantly outperforming existing methods.

Optimal Robotic Assembly Sequence Planning: A Sequential Decision-Making Approach

The optimal robot assembly planning problem is challenging due to the necessity of finding the optimal solution amongst an exponentially vast number of possible plans, all while satisfying a selection of constraints. Traditionally, robotic assembly planning problems have been solved using heuristics, but these methods are specific to a given objective structure or set of problem parameters. In this paper, we propose a novel approach to robotic assembly planning that poses assembly sequencing as a sequential decision making problem, enabling us to harness methods that far outperform the state-of-the-art. We formulate the problem as a Markov Decision Process (MDP) and utilize Dynamic Programming (DP) to find optimal assembly policies for moderately sized strictures. We further expand our framework to exploit the deterministic nature of assembly planning and introduce a class of optimal Graph Exploration Assembly Planners (GEAPs). For larger structures, we show how Reinforcement Learning (RL) enables us to learn policies that generate high reward assembly sequences. We evaluate our approach on a variety of robotic assembly problems, such as the assembly of the Hubble Space Telescope, the International Space Station, and the James Webb Space Telescope. We further showcase how our DP, GEAP, and RL implementations are capable of finding optimal solutions under a variety of different objective functions and how our formulation allows us to translate precedence constraints to branch pruning and thus further improve performance. We have published our code at https://github.com/labicon/ORASP-Code.