RAMP: A Benchmark for Evaluating Robotic Assembly Manipulation and Planning
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Abstract (arXiv)
We introduce RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. RAMP consists of beams that a robot must assemble into specified goal configurations using pegs as fasteners. As such, it assesses planning and execution capabilities, and poses challenges in perception, reasoning, manipulation, diagnostics, fault recovery, and goal parsing. RAMP has been designed to be accessible and extensible. Parts are either 3D printed or otherwise constructed from materials that are readily obtainable. The design of parts and detailed instructions are publicly available. In order to broaden community engagement, RAMP incorporates fixtures such as April Tags which enable researchers to focus on individual sub-tasks of the assembly challenge if desired. We provide a full digital twin as well as rudimentary baselines to enable rapid progress. Our vision is for RAMP to form the substrate for a community-driven endeavour that evolves as capability matures.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 May 2023.
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Citing papers per year: 2024: 3; 2025: 4; 2026: 2.
9 papers.
- 2025RoboArena: Distributed Real-World Evaluation of Generalist Robot Policiescited by 63
- 2025Reliable and Scalable Robot Policy Evaluation with Imperfect Simulatorscited by 13
- 2024SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicingcited by 6
- 2025NEBULA: Do We Evaluate Vision-Language-Action Agents Correctly?cited by 6
- 2026Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulationcited by 5
- 2026VLA-REPLICA: A Low-Cost, Reproducible Benchmark for Real-World Evaluation of Vision-Language-Action Modelscited by 2
- 2024COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertaintycited by 0
- 2024Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Conceptscited by 0
- 2025COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Graspingcited by 0
- 2026VLA-REPLICA: A Low-Cost, Reproducible Benchmark for Real-World Evaluation of Vision-Language-Action Models20 May 2026
- 2026Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation27 Feb 2026
- 2025NEBULA: Do We Evaluate Vision-Language-Action Agents Correctly?17 Oct 2025
- 2025Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators5 Oct 2025
- 2025RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies22 Jun 2025
- 2025COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping12 Feb 2025
- 2024Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts11 Apr 2024
- 2024SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing3 Apr 2024
- 2024COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty21 Mar 2024