The paper presents Language Instruction grounding for Motion Planning (LIMP), a system that enables robots to follow complex language instructions by generating semantic maps. LIMP uses foundation models and temporal…
Continue readingThe paper presents a combined task-level reinforcement learning and motion planning framework to solve a multi-class in-rack test tube rearrangement problem. The framework uses reinforcement learning at the task level…
Continue readingThe paper introduces a new approach (CBNNTAP) that addresses the complexities and challenges presented by ocean currents when optimizing target assignment and motion planning for a multi-unmanned underwater vehicle (UUV)…
Continue readingThe paper presents the CBNNTAP algorithm, an innovative approach to optimize target assignment and motion planning for a multi-unmanned underwater vehicle (UUV) system. The algorithm incorporates a bio-inspired neural network-based…
Continue readingThe paper discusses the development of a soft-constrained iterative linear quadratic regulator (soft-CILQR) algorithm for autonomous vehicle applications. The algorithm integrates the CILQR algorithm and a model predictive control constraint…
Continue readingThis study introduces a method for multi-robot motion planning that combines centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics. The method has been tested on a range…
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