The paper introduces D&A (Describe & Assess), a prompt-based method that separates progress estimation into a textual description of scene dynamics and a subsequent assessment using explicit rubrics. By requiring Vision‑Language Models to generate an explicit definition of absolute success before scoring, D&A anchors evaluations to an external criterion, reducing confirmation bias and providing fine‑grained, partial progress metrics. Empirical results show that D&A matches or surpasses post‑trained reward models across tasks, embodiments, and benchmarks, and its reward estimates support efficient downstream reinforcement learning.
By Marius Memmel, Shawn Yin, Urvi Rutia, Angad Wadhwa, Eldon Li, Gavin Angeloff, Ankit Goyal, Jesse Zhang, Dieter Fox, Fabio Ramos, Anqi Li, Abhishek Gupta
arXiv:2602. 19313v2 Announce Type: replace-cross Abstract: General-purpose robot learning requires dense, instruction-conditioned feedback that can distinguish meaningful task progress from stalled, failed, or partially completed behavior.
By Shirui Chen, Cole Harrison, Ying-Chun Lee, Angela Jin Yang, Zhongzheng Ren, Lillian J. Ratliff, Jiafei Duan, Dieter Fox, Ranjay Krishna
arXiv:2606. 00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics.
By Christian Gumbsch, Leonardo Barcellona, Lennard Sch\"unemann, Platon Karageorgis, Andrii Zadaianchuk, Zehao Wang, Sergey Zakharov, Fabien Despinoy, Rahaf Aljundi, Efstratios Gavves
RM‑VLM introduces a method to generate reward machines (RMs) from natural‑language task descriptions using a large language model, and employs a vision‑language model as the labeling function to ground RM propositions over image observations. This approach eliminates the need for manually engineered labeling functions, enabling RM‑guided policy learning across diverse domains. Experiments on grid‑world navigation, robotic manipulation, and 3D object collection demonstrate that RM‑VLM consistently outperforms existing VLM‑based reward methods in task success rates.
By Roko Parac, Christos Ziakas, Leo Ardon, Alessandra Russo
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task ca...
Zero-WAM introduces a causal video-action model that enables robots to perform unseen manipulation tasks by following in-context human video guidance. The authors create HumanGen, a dataset of 74.2K human-robot ICL pairs across 8.6K tasks, and propose an in-context future chunk prediction objective to prevent shortcut learning. In simulation, Zero-WAM attains a 47.0% success rate on seven unseen tasks, outperforming the best video-action baseline by 29.5 percentage points, and demonstrates real‑world generalization to complex, long‑horizon, and fine‑grained tasks.
By Jiaming Zhou, Qihang Zhang, Gangwei Xu, Cunxin Fan, Yujie Zhao, Ruilin Wang, Yiming Luo, Shuai Yang, Xing Zhu, Yujun Shen, Junwei Liang, Yinghao Xu