Monte Carlo Tree Search with Tensor Factorization for Robot Optimization

TL;DR

This paper introduces Tensor Train Tree Search (TTTS), a novel method that enhances Monte Carlo Tree Search for robotic optimization by exploiting tensor factorization to reduce complexity and improve efficiency across various robotic tasks.

Contribution

The paper presents TTTS, a tensor factorization-based enhancement to Monte Carlo Tree Search, enabling efficient and scalable optimization in complex robotic decision-making problems.

Findings

TTTS reduces computation time and memory usage significantly.

TTTS can efficiently find bounded global optima.

Experimental results show improved performance across diverse robotic tasks.

Abstract

Many robotic tasks, such as inverse kinematics, motion planning, and optimal control, can be formulated as optimization problems. Solving these problems involves addressing nonlinear kinematics, complex contact dynamics, long-horizon correlation, and multi-modal landscapes, each posing distinct challenges for state-of-the-art optimization methods. Monte Carlo Tree Search is a powerful approach that can strategically explore the solution space and can be applied to a wide range of tasks across varying scenarios. However, it typically suffers from combinatorial complexity when applied to robotics, resulting in slow convergence and high memory demands. To address this limitation, we propose \emph{Tensor Train Tree Search} (TTTS), which leverages tensor factorization to exploit correlations among decision variables arising from common kinematic structures, dynamic constraints, and environmental interactions in robot decision-making. This yields a compact, linear-complexity representation that significantly reduces both computation time and storage requirements. We prove that TTTS can efficiently reach the bounded global optimum within a finite time. Experimental results across inverse kinematics, motion planning around obstacles, legged robot manipulation, multi-stage motion planning, and bimanual whole-body manipulation demonstrate the efficiency of TTTS on a diverse set of robotic tasks.