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About

Hiroyuki Oyama is a roboticist whose research focuses on bridging the gap between high-level task specifications and low-level motion execution, particularly for sequential manipulation. His core contributions lie in optimization-based task and motion planning (TAMP), where he integrates formal methods like Signal Temporal Logic (STL) and Linear Temporal Logic (LTL) to generate collision-free, executable plans for pick-and-place and dual-arm manipulation tasks. His most cited work, "Continuous Optimization-Based Task and Motion Planning with Signal Temporal Logic Specifications for Sequential Manipulation" (2021, 20 citations), proposes a novel framework that unifies task planning and trajectory optimization under temporal constraints. This is complemented by his work on fast LTL-based planning for dual-arm systems (19 citations) and efficient MILP-based TAMP for pick-and-place with hard/soft collision constraints (8 citations). Oyama also explores robot skill learning, using level set estimation to identify preconditions and postconditions for hierarchical task decomposition. His early work on bipedal running robots, controlled via a central pattern generator (CPG) on the "KenkenII" platform, demonstrates a long-standing interest in adaptive, underactuated systems. With a growing citation record and a focus on computationally efficient, formally-grounded planning, Oyama is contributing to the practical deployment of autonomous robots in complex, real-world environments.