This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.
Monte Carlo tree search (MCTS) @cite_38 has become popular for real-time decisions in AI. It is an online alternative to dynamic programming and uses repeated simulations to construct a tree in a best-first order. Kocsic and Szepesv a ri @cite_21 showed that tree policy using the UCT (Upper Confidence bounds applied to Trees) guarantees asymptotic optimality. Feldman and Domshlak @cite_30 introduced BRUE, a purely exploring MCTS that guarantees exponential cost reduction. Silver and Veness @cite_27 extended MCTS to partially-observable models. MCTS has been used for game solving @cite_36 and belief-space planning in robotics @cite_34 @cite_24 @cite_0 , but has not been applied to manipulation.
@cite_30 @cite_38 @cite_36 @cite_21 @cite_24 @cite_0 @cite_27 @cite_34
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Monte Carlo tree search (MCTS) has brought about great success regarding the evaluation of stochastic and deterministic games in recent years. We present and empirically analyze a data-driven parallelization approach for MCTS targeting large HPC clusters with Infiniband interconnect. Our implementation is based on OpenMPI and makes extensive use of its RDMA based asynchronous tiny message communication capabilities for effectively overlapping communication and computation. We integrate our parallel MCTS approach termed UCT-Treesplit in our state-of-the-art Go engine Gomorra and measure its strengths and limitations in a real-world setting. Our extensive experiments show that we can scale up to 128 compute nodes and 2048 cores in self-play experiments and, furthermore, give promising directions for additional improvement. The generality of our parallelization approach advocates its use to significantly improve the search quality of a huge number of current MCTS applications. Monte-Carlo Tree Search (MCTS) is a successful approach for improving the performance of game-playing programs. This paper presents the Accelerated UCT algorithm, which overcomes a weakness of MCTS caused by deceptive structures which often appear in game tree search. It consists in using a new backup operator that assigns higher weights to recently visited actions, and lower weights to actions that have not been visited for a long time. Results in Othello, Havannah, and Go show that Accelerated UCT is not only more effective than previous approaches but also improves the strength of Fuego, which is one of the best computer Go programs. Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS has been notoriously difficult to scale well, especially in distributed environments. For a distributed parallel search, transposition-table driven scheduling (TDS) is known to be efficient in several domains. We present a massively parallel MCTS algorithm, that applies the TDS parallelism to the Upper Confidence bound Applied to Trees (UCT) algorithm, which is the most representative MCTS algorithm. To drastically decrease communication overhead, we introduce a reformulation of UCT called Depth-First UCT. The parallel performance of the algorithm is evaluated on clusters using up to 1,200 cores in artificial game-trees. We show that this approach scales well, achieving 740-fold speedups in the best case. Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search. The motivation behind this work is caused by the emerging GPU-based systems and their high computational potential combined with relatively low power usage compared to CPUs. As a problem to be solved I chose to develop an AI GPU(Graphics Processing Unit)-based agent in the game of Reversi (Othello) which provides a sufficiently complex problem for tree searching with non-uniform structure and an average branching factor of over 8. I present an efficient parallel GPU MCTS implementation based on the introduced 'block-parallelism' scheme which combines GPU SIMD thread groups and performs independent searches without any need of intra-GPU or inter-GPU communication. I compare it with a simple leaf parallel scheme which implies certain performance limitations. The obtained results show that using my GPU MCTS implementation on the TSUBAME 2.0 system one GPU can be compared to 100-200 CPU threads depending on factors such as the search time and other MCTS parameters in terms of obtained results. I propose and analyze simultaneous CPU GPU execution which improves the overall result.