Robot Learning

Tree Search

Tree search is a family of planning methods that explore future possibilities by expanding a tree of states reachable through action sequences, evaluating branches to select the best next action. Variants include classical A* and minimax as well as Monte Carlo Tree Search (MCTS), which balances exploration and exploitation through sampled rollouts and powered AlphaGo and AlphaZero when paired with learned value and policy networks. In robotics, tree search appears in task planning, TAMP, and search over learned world models.

Why it matters for physical AI

Deliberate look-ahead over a learned world model offers a route past the reactive limits of behavior-cloned policies, and search-augmented decision-making is an active direction for improving long-horizon reliability in robot systems.