Monte Carlo Tree Search with Tensor Factorization for Optimization Problems in Robotics
For roboticists, TTTS provides a general-purpose optimizer that reduces engineering effort and improves cross-task generalization, though it is an incremental improvement over existing MCTS methods.
The paper introduces Tensor Train Tree Search (TTTS), which combines Monte Carlo Tree Search with tensor factorization to efficiently solve diverse robotic optimization problems. TTTS achieves significant reductions in computation and storage overhead, enabling efficient global decision making across tasks like inverse kinematics, motion planning, and manipulation.
Many robotic tasks, such as inverse kinematics, motion planning, and contact-rich manipulation, can be formulated as optimization problems. Solving these problems requires addressing inherent nonlinear kinematics, complex contact dynamics, long-horizon correlations, and multi-modal optimization landscapes, each posing distinct challenges for state-of-the-art optimizers. While existing methods tackle these issues through problem-specific strategies, such specialization inherently limits cross-task generalization, requires heavy engineering effort in problem reformulation, and hinders multi-task autonomy. Monte Carlo Tree Search (MCTS) offers a compelling framework that generalizes across diverse robotic tasks via strategic exploration of the solution space. However, it typically suffers from combinatorial complexity when applied naively, resulting in slow convergence and excessive storage space in high-dimensional domains. To address this limitation, we propose Tensor Train Tree Search (TTTS), which leverages tensor factorization to exploit implicit correlations among different branches within the decision tree. By utilizing the resulting compact, linear-complexity representation, TTTS significantly reduces both computation and storage overhead, thereby enabling highly efficient global decision making. 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 for generalized robot optimization over a diverse set of tasks.