From LLM-Generated Specifications to Learned Quadruped Locomotion
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2607. 00442v1 Announce Type: cross Abstract: Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that limit both interpretability of learned policies and lack explicit control over gait behaviors.
arXiv:2608.31167v1 Announce Type: cross Abstract: Bridging model-based control and learned policies in long-horizon manipulation has harbored a silent disagreement: control executes specified objecti...
arXiv:2605. 30719v2 Announce Type: replace-cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.
arXiv:2607. 24083v1 Announce Type: new Abstract: Reinforcement learning can produce robust humanoid controllers, but each new task is typically trained as a separate policy with its own reward design and training process.
The paper presents a decentralized navigation framework for composite heterogeneous robots that integrates a large language model (LLM) policy agent, an Upper Confidence Bound (UCB) bandit, and a Double Deep Q-Network (Double DQN) controller. Each robot independently generates and refines policies at the round level using LLM inference, while the Double DQN handles tick-level action selection based on navigation variables and LLM priors. Across 30 rounds, the full configuration achieved all goals with the lowest median completion time (42 ticks) and a 25–39% improvement over other setups.
arXiv:2608. 13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime.