Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design
CoRR(2024)
摘要
Deep reinforcement learning (DRL) has emerged as a promising solution to
mastering explosive and versatile quadrupedal jumping skills. However, current
DRL-based frameworks usually rely on well-defined reference trajectories, which
are obtained by capturing animal motions or transferring experience from
existing controllers. This work explores the possibility of learning dynamic
jumping without imitating a reference trajectory. To this end, we incorporate a
curriculum design into DRL so as to accomplish challenging tasks progressively.
Starting from a vertical in-place jump, we then generalize the learned policy
to forward and diagonal jumps and, finally, learn to jump across obstacles.
Conditioned on the desired landing location, orientation, and obstacle
dimensions, the proposed approach contributes to a wide range of jumping
motions, including omnidirectional jumping and robust jumping, alleviating the
effort to extract references in advance. Particularly, without constraints from
the reference motion, a 90cm forward jump is achieved, exceeding previous
records for similar robots reported in the existing literature. Additionally,
continuous jumping on the soft grassy floor is accomplished, even when it is
not encountered in the training stage. A supplementary video showing our
results can be found at https://youtu.be/nRaMCrwU5X8 .
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