[2603.02115] Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons

Computer Science > Robotics arXiv:2603.02115 (cs) [Submitted on 2 Mar 2026] Title:Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons Authors:Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sidhant Kaushik, Aditya Shah, Alex S. Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, Jesse Zhang View a PDF of the paper titled Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons, by Anthony Liang and 16 other authors View PDF HTML (experimental) Abstract:General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effective for expert demonstrations, this paradigm scales poorly to large-scale robotics datasets where failed and suboptimal trajectories are abundant and assigning dense progress labels is ambiguous. We introduce Robometer, a scalable reward modeling framework that combines intra-trajectory progress supervision with inter-trajectory preference supervision. Robometer is trained with a dual objective: a frame-level progress loss that anchors reward magnitude on expert data, and a trajectory-comparison preference loss that imposes global ordering constraints across trajectories of the same task, enabling effective learning from both real and augmented failed trajectories. To support this formulation at scale, we cur...