Tan Sang Nguyen, Yi Ru Wang, Shailesh Shailesh, Gang Yang, Jiamo Hu, Jianuo Jiang, Haoquan Fang, Zhongzheng Ren 等 27 位作者
General-purpose robot policies must work across robots, tasks, and environments, yet the variation that makes physical trials informative also makes them difficult to reproduce. We introduce RoboColosseum, a distributed evaluation framework that combines unified policy serving and standardized protocols across a network of locally operated robots in diverse environments. Our initial study evaluates five vision-language-action (VLA) policies and three frontier AI agents across four embodiments (SO-101, Franka, Bimanual-YAM, and G1 Humanoid). The Open Track compares models without task-specific adaptation in blinded, pairwise trials under matched local conditions. The Fine-tune Track measures how well VLA policies adapt to 20 tasks across all four embodiments when each policy receives the same 100 demonstrations per task. Across 5,699 pairwise Open Track comparisons at 124 sites, frontier agents rival specialized VLA policies, while all systems struggle to complete open-world tasks reliably. After fine-tuning, GR00T N1.7 leads on SO-101 and pi0.5 leads on Franka and Bimanual-YAM. Policy rankings show weak agreement across embodiment-specific task suites (Kendall W = 0.24). Open Track rankings poorly predict relative performance after fine-tuning, with substantial rank reversals on all three embodiments shared between tracks. Measuring partial progress reveals differences that task success alone misses. On the G1 Humanoid, no policy completes any of the five tasks, yet average progress ranges from 13.3% to 23.2% across policies. Open Track rankings stabilize as trials accumulate, and active matchmaking achieves comparable ranking agreement with substantially fewer physical trials than uniform sampling. These results show how distributed physical evaluation can reveal where robot policies generalize, how well they adapt to new tasks, and how far they progress on tasks they fail to complete.
Hanbo Zhang, Tianrun Hu, Cedar Site Bai, Anxing Xiao, David Hsu
Visual grounding is crucial for robots to understand language instructions with visual observations. This paper addresses the problem of visual grounding uncertainties, where a robot uses a black-box vision-language model (VLM) to ground free-form natural-language instructions to target objects. Prevailing methods derive uncertainty from the VLM itself. Yet, outputs of the VLM, e.g., generated uncertainties, are from the same parameters and structures producing the predictions, and inherit the bias. We give a formal Bayesian definition of the problem that treats the VLM prediction as an observation. An external surrogate Bayesian model then quantifies per-query uncertainty over a local neighborhood of a held-out calibration set, decomposed into aleatoric and epistemic components. A hierarchical retrieval across input modalities realizes a compact joint-space neighborhood, mitigating the curse of dimensionality. The estimator further integrates with conformal prediction or model-based planning for active visual grounding, where robots are allowed to take verbal or physical actions to ground targets. Across grounding benchmarks and state-of-the-art VLMs, it consistently improves both calibration and task performance. On a real Fetch robot under open-world instructions, it enables principled trade-offs between confident action and information gathering, substantially improving robustness.
We consider mobile grasping in unknown and dynamic environments, where a robot must approach and grasp a target object using a camera with a limited field of view. This setting introduces two visibility constraints. Incomplete environmental observations can create collision risks along the whole-body trajectory, while occlusions can hinder object pose refinement during grasping. Previous systems often assume known or static environments and decouple perception from motion, leaving safety unresolved around unobserved obstacles. We present a unified system that enforces both visibility constraints at runtime through three core components. A velocity-aware gaze policy directs perception toward fast-moving regions of the planned swept volume, a hierarchical subgoal policy samples grasp and intermediate configurations that maintain target visibility, and a real-time whole-body planner replans over a receding horizon as the map updates. Across 400 randomized simulation scenarios and real-world trials on a Fetch mobile manipulator, our system achieves success rates of 70.4% in unknown static environments and 57.9% in dynamic environments, outperforming the decoupled navigation- and-manipulation strategy by 16.3 and 9.3 percentage points, respectively, while significantly reducing collisions.