REALM: A Real-to-Sim Validated Benchmark for Generalization in Robotic Manipulation

• URL: http://arxiv.org/abs/2512.19562v1
• Date: Mon, 22 Dec 2025 16:44:23 GMT
• Title: REALM: A Real-to-Sim Validated Benchmark for Generalization in Robotic Manipulation
• Authors: Martin Sedlacek, Pavlo Yefanov, Georgy Ponimatkin, Jai Bardhan, Simon Pilc, Mederic Fourmy, Evangelos Kazakos, Cees G. M. Snoek, Josef Sivic, Vladimir Petrik,
• Abstract summary: Vision-Language-Action (VLA) models empower robots to understand and execute tasks described by natural language instructions.<n>A key challenge lies in their ability to generalize beyond the specific environments and conditions they were trained on.<n>We present REALM, a new simulation environment and benchmark designed to evaluate the generalization capabilities of VLA models.
• Score: 46.970355660265284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models empower robots to understand and execute tasks described by natural language instructions. However, a key challenge lies in their ability to generalize beyond the specific environments and conditions they were trained on, which is presently difficult and expensive to evaluate in the real-world. To address this gap, we present REALM, a new simulation environment and benchmark designed to evaluate the generalization capabilities of VLA models, with a specific emphasis on establishing a strong correlation between simulated and real-world performance through high-fidelity visuals and aligned robot control. Our environment offers a suite of 15 perturbation factors, 7 manipulation skills, and more than 3,500 objects. Finally, we establish two task sets that form our benchmark and evaluate the π_{0}, π_{0}-FAST, and GR00T N1.5 VLA models, showing that generalization and robustness remain an open challenge. More broadly, we also show that simulation gives us a valuable proxy for the real-world and allows us to systematically probe for and quantify the weaknesses and failure modes of VLAs. Project page: https://martin-sedlacek.com/realm

Related papers

• Self-Correcting VLA: Online Action Refinement via Sparse World Imagination [55.982504915794514]
  We propose Self-Correcting VLA (SC-VLA), which achieve self-improvement by intrinsically guiding action refinement through sparse imagination.<n>SC-VLA achieve state-of-the-art performance, yielding the highest task throughput with 16% fewer steps and a 9% higher success rate than the best-performing baselines.
  arXiv  Detail & Related papers  (2026-02-25T06:58:06Z)
• SimVLA: A Simple VLA Baseline for Robotic Manipulation [46.38114519538192]
  Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic manipulation.<n>We introduce SimVLA, a streamlined baseline designed to establish a transparent reference point for VLA research.
  arXiv  Detail & Related papers  (2026-02-20T14:04:27Z)
• VLNVerse: A Benchmark for Vision-Language Navigation with Versatile, Embodied, Realistic Simulation and Evaluation [61.82502719679122]
  We introduce VLNVerse, a benchmark for Versatile, Embodied, Realistic Simulation, and Evaluation.<n>VLNVerse redefines VLN as a scalable, full-stack embodied AI problem.<n>We propose a novel unified multi-task model capable of addressing all tasks within the benchmark.
  arXiv  Detail & Related papers  (2025-12-22T04:27:26Z)
• PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies [88.78188489161028]
  We introduce Policy Evaluation and Environment Reconstruction in Simulation (PolaRiS)<n>PolaRiS is a scalable real-to-sim framework for high-fidelity simulated robot evaluation.<n>We show that PolaRiS evaluations provide a much stronger correlation to real world generalist policy performance than existing simulated benchmarks.
  arXiv  Detail & Related papers  (2025-12-18T18:49:41Z)
• WMPO: World Model-based Policy Optimization for Vision-Language-Action Models [22.01666177489494]
  Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation.<n>We introduce World-Model-based Policy Optimization (WMPO), a principled framework for on-policy VLA without interacting with the real environment.
  arXiv  Detail & Related papers  (2025-11-12T17:54:09Z)
• RobotArena $\infty$: Scalable Robot Benchmarking via Real-to-Sim Translation [47.79800816696372]
  Real-world testing of manipulation policies is labor-intensive at scale, and difficult to reproduce.<n>Existing simulation benchmarks are similarly limited, as they train and test policies within the same synthetic domains.<n>In this paper, we introduce a new benchmarking framework that overcomes these challenges by shifting VLA evaluation into large-scale simulated augmented environments.
  arXiv  Detail & Related papers  (2025-10-27T17:41:38Z)
• HA-VLN 2.0: An Open Benchmark and Leaderboard for Human-Aware Navigation in Discrete and Continuous Environments with Dynamic Multi-Human Interactions [64.69468932145234]
  We present HA-VLN 2.0, a unified benchmark introducing explicit social-awareness constraints.<n>Results show that explicit social modeling improves navigation robustness and reduces collisions.
  arXiv  Detail & Related papers  (2025-03-18T13:05:55Z)
• VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks [100.3234156027118]
  We present VLABench, an open-source benchmark for evaluating universal LCM task learning.<n>VLABench provides 100 carefully designed categories of tasks, with strong randomization in each category of task and a total of 2000+ objects.<n>The benchmark assesses multiple competencies including understanding of mesh&texture, spatial relationship, semantic instruction, physical laws, knowledge transfer and reasoning.
  arXiv  Detail & Related papers  (2024-12-24T06:03:42Z)
• Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data [26.004807291215258]
  Language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks. We propose a general-purpose, language-conditioned approach that combines base skill priors and imitation learning under unstructured data. We assess our model's performance in both simulated and real-world environments using a zero-shot setting.
  arXiv  Detail & Related papers  (2023-05-30T14:40:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.