The paper introduces RoboImagine, a framework that refines autoregressive robotic world models by comparing candidate future trajectories using both semantic and visual feedback. It employs diverse search to generate varied candidate paths, a frozen multimodal evaluator for task‑relevant semantic preferences, and pixel‑level visual consistency checks to guide policy optimization. Experiments on RT‑1, BridgeV2, and RoboCasa show that RoboImagine improves visual fidelity (e.g., PSNR gains) and task relevance compared to standard fine‑tuning, with ablations confirming the benefits of diverse search and combined feedback signals.
By Hao Wu, Yuqi Li, Yizhou Zhao, Kun Wang, Xiao Luo, Yu Cheng, Bo An, Yingli Tian, Laszlo A. Jeni, Ming-Hsuan Yang
arXiv:2606. 15631v1 Announce Type: cross Abstract: Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute.
By Jeongeun Park, Juhan Park, Taekyung Kim, Sungjoon Choi, Dongyoon Han, Sangdoo Yun
arXiv:2603.16065v3 Announce Type: replace-cross
Abstract: Reinforcement Learning (RL) has shown strong potential for improving robotic manipulation policies, yet its practical use remains bottlenecke...
By Yanru Wu, Weiduo Yuan, Esteban Martinez Licon, Ang Qi, Vitor Guizilini, Jiageng Mao, Yue Wang
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
Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and ro...
The paper introduces D&A (Describe & Assess), a prompt‑based method that separates vision‑language model progress estimation into a textual description of scene dynamics and a rubric‑based assessment. By requiring the model to explicitly define absolute success before scoring, D&A reduces confirmation bias and provides fine‑grained, partial progress metrics that accurately penalize failures and interpolate successes. The approach generalizes across tasks, embodiments, and benchmarks, matching or surpassing post‑trained reward models while enabling efficient downstream reinforcement learning.
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:2609.10506v1 Announce Type: cross
Abstract: Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However...
By Nisarga Nilavadi, Ralf R\"omer, Moritz Reuss, Michael Krawez, Tobias J\"ulg, Angela P. Schoellig, Rudolf Lioutikov, Wolfram Burgard
InfiNoVA is a data‑augmentation framework that transforms synchronized multi‑camera demonstrations into a dense, geometrically consistent set of training views by reconstructing each manipulation trajectory as a time‑varying 3D Gaussian. The method renders novel observations from sampled camera poses while preserving the original state‑action pairs, improving frame‑level fidelity and temporal consistency compared to generative synthesis. Across four real‑world manipulation tasks, policies trained with InfiNoVA achieve 5.4× higher average success under unseen randomized viewpoints than VISTA‑based augmentation and 1.7× higher success than training on all five physical camera views.
By Sai Puneeth Reddy Gottam, Elmar Rueckert, Vedant Dave
arXiv:2606. 27872v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation.
By Zhipeng Xie, Zongyi Han, Xiangyi Wei, Shiliang Sun, Yang Li, Jing Zhao
arXiv:2607. 20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently.
By Arjun Majumdar, Avinash Sooriyarachchi, Benjamin Tibi, Chris Bamford, Elliot Chane-Sane, Guillaume Lample, Khyathi Raghavi Chandu, Ludovic Ho Fuh, Mathieu Poiree, Olivier Duchenne, Rosalie Millner, Srijan Mishra, Theo Cachet, Thomas Chabal
The paper introduces RARM, a Reference‑Anchored Reward Model that uses a single successful demonstration to generate dense, progress‑aware rewards for reinforcement learning in robot manipulation. RARM is trained on general‑purpose videos with a contrastive temporal objective, requiring no task‑specific data or reward engineering. During deployment it matches rollout clips to reference clips and rewards only confident forward progress, reducing false positives. Experiments on nine simulated tasks and four real‑world tasks show that RARM achieves the best overall success rates, especially on long‑horizon tasks like cloth folding.
By Pengzhi Yang, Xinyu Wang, Pengyu Jing, Kehan Wen, Yiduo Qu, Zhenhao Huang, Minghao Fu, Xin Liu, Yaheng Shen, Fan Shi