Higher-Order Action Supervision Makes A Strong Policy Class
Modern data-driven decision-making methods, such as imitation learning (IL) and reinforcement learning (RL), have achieved great success in solving many complex tasks. However, these methods often suffer from serious control instability and robustness issues when applied in real-world applications such as robotics and autonomous driving, posing notable challenges for their practical deployment. We argue that this instability issue stems largely from their limitations in solely supervising and optimizing zeroth-order actions (i.e., the action labels), failing to account for higher-order action dynamics and temporal consistency. In this paper, we show that simultaneously supervising both ze...
| Authors | Peng Cheng, Yunxian Hou, Zhi Zhou et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 October 2026 |
| Last updated at source | 9 October 2026 |
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