RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
<|reference_start|>RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards: This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable number of partial or complete pose targets that are spaced arbitrarily in time. Our proposed framework utilizes a multi-critic reinforcement learning algorithm to effectively handle the mixture of dense and sparse rewards. Additionally, it employs a transformer-based encoder to accommodate a variable number of input targets, each associated with specific time-to-arrivals. Throughout simulation and hardware experiments, we demonstrate that our framework can effectively satisfy the target keyframe sequence at the required times. In the experiments, the multi-critic method significantly reduces the effort of hyperparameter tuning compared to the standard single-critic alternative. Moreover, the proposed transformer-based architecture enables robots to anticipate future goals, which results in quantitative improvements in their ability to reach their targets.<|reference_end|>
@article{zargarbashi2024robotkeyframing:,
    title={RobotKeyframing: Learning Locomotion with High-Level Objectives via
  Mixture of Dense and Sparse Rewards},
    author={Fatemeh Zargarbashi, Jin Cheng, Dongho Kang, Robert Sumner, Stelian
  Coros},
    journal={arXiv preprint arXiv:2407.11562},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11562},
    primaryClass={cs.RO cs.AI cs.LG}
}