Generalizable Dense Reward for Long-Horizon Robotic Tasks
Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long-horizon tasks due to distribution shift and error accumulation. While reinforcement learning (RL) can finetune these models, it cannot work well across diverse tasks without manual reward engineering. We propose VLLR, a dense reward framework combining (1) an extrinsic reward from Large Language Models (LLMs) and Vision-Language Models (VLMs) for task progress recognition, and (2) an intrinsic reward based on policy self-certainty. VLLR uses LLMs to decompose tasks into verifiable subtasks and then VLMs to estimate ...
| Authors | Silong Yong, Stephen Sheng, Carl Qi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 31 March 2026 |
| Last updated at source | 5 October 2026 |
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