AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

• URL: http://arxiv.org/abs/2604.11674v1
• Date: Mon, 13 Apr 2026 16:21:44 GMT
• Title: AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation
• Abstract summary: AffordSim is a simulation framework that integrates open-vocabulary 3D affordance prediction into the manipulation data generation pipeline.<n>AffordSim uses our VoxAfford model, an open-vocabulary 3D affordance detector, to predict affordance maps on object point clouds.<n>Built on NVIDIA Isaac Sim with cross-embodiment support, AffordSim enables automated, scalable generation of affordance-aware manipulation data.
• Score: 42.842219862127116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation-based data generation has become a dominant paradigm for training robotic manipulation policies, yet existing platforms do not incorporate object affordance information into trajectory generation. As a result, tasks requiring precise interaction with specific functional regions--grasping a mug by its handle, pouring from a cup's rim, or hanging a mug on a hook--cannot be automatically generated with semantically correct trajectories. We introduce AffordSim, the first simulation framework that integrates open-vocabulary 3D affordance prediction into the manipulation data generation pipeline. AffordSim uses our VoxAfford model, an open-vocabulary 3D affordance detector that enhances MLLM output tokens with multi-scale geometric features, to predict affordance maps on object point clouds, guiding grasp pose estimation toward task-relevant functional regions. Built on NVIDIA Isaac Sim with cross-embodiment support (Franka FR3, Panda, UR5e, Kinova), VLM-powered task generation, and novel domain randomization using DA3-based 3D Gaussian reconstruction from real photographs, AffordSim enables automated, scalable generation of affordance-aware manipulation data. We establish a benchmark of 50 tasks across 7 categories (grasping, placing, stacking, pushing/pulling, pouring, mug hanging, long-horizon composite) and evaluate 4 imitation learning baselines (BC, Diffusion Policy, ACT, Pi 0.5). Our results reveal that while grasping is largely solved (53-93% success), affordance-demanding tasks such as pouring into narrow containers (1-43%) and mug hanging (0-47%) remain significantly more challenging for current imitation learning methods, highlighting the need for affordance-aware data generation. Zero-shot sim-to-real experiments on a real Franka FR3 validate the transferability of the generated data.

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