From planning to policy: distilling $\texttt{Skill-RRT}$ for long-horizon prehensile and non-prehensile manipulation

Current robots face challenges in manipulation tasks that require a long sequence of prehensile and non-prehensile skills. This involves handling contact-rich interactions and chaining multiple skills while considering their long-term consequences. This paper presents a framework that leverages imitation learning to distill a planning algorithm, capable of solving long-horizon problems but requiring extensive computation time, into a policy for efficient action inference. We introduce $\texttt{Skill-RRT}$, an extension of the rapidly-exploring random tree (RRT) that incorporates skill applicability checks and intermediate object pose sampling for efficient long-horizon planning. To enable skill chaining, we propose $\textit{connectors}$, goal-conditioned policies that transition between skills while minimizing object disturbance. Using lazy planning, connectors are selectively trained on relevant transitions, reducing the cost of training. High-quality demonstrations are generated with $\texttt{Skill-RRT}$ and refined by a noise-based replay mechanism to ensure robust policy performance. The distilled policy, trained entirely in simulation, zero-shot transfer to the real world, and achieves over 80% success rates across three challenging manipulation tasks. In simulation, our approach outperforms the state-of-the-art skill-based reinforcement learning method, $\texttt{MAPLE}$, and $\texttt{Skill-RRT}$.

Learning context-aware adaptive solvers to accelerate quadratic programming

Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The alternating direction method of multipliers (ADMM) is a successful method to solve QP. Even though ADMM shows promising results in solving various types of QP, its convergence speed is known to be highly dependent on the step-size parameter $\rho$. Due to the absence of a general rule for setting $\rho$, it is often tuned manually or heuristically. In this paper, we propose CA-ADMM (Context-aware Adaptive ADMM)) which learns to adaptively adjust $\rho$ to accelerate ADMM. CA-ADMM extracts the spatio-temporal context, which captures the dependency of the primal and dual variables of QP and their temporal evolution during the ADMM iterations. CA-ADMM chooses $\rho$ based on the extracted context. Through extensive numerical experiments, we validated that CA-ADMM effectively generalizes to unseen QP problems with different sizes and classes (i.e., having different QP parameter structures). Furthermore, we verified that CA-ADMM could dynamically adjust $\rho$ considering the stage of the optimization process to accelerate the convergence speed further.