Why a grasp can pass the test but still be wrong: the contact-topology blind spot
The core issue is that many grasp synthesis tests measure whether a grasp is feasible—e.g., whether the hand doesn't intersect the object and can hold it—but they don't deeply evaluate the contact topology: the specific pattern of which fingertips touch which parts of the object and how those contacts distribute force. A 2025 study (Diffangle-Grasp) showed that even a strong baseline model had significant contact-map reconstruction error, and their improved method only reduced that error by 9.59% [1]. That means the model's predicted contact maps were still noticeably off, yet the grasp might still be classified as successful in a standard test. Similarly, GrainGrasp (2024) argues that generating a single global contact map is insufficient; they propose separate contact maps for each fingertip to capture fine-grained finger-object interactions [5]. The implication: if a test only checks overall success, it can miss that the contact topology is unrealistic—e.g., fingers touching in awkward spots or with poor force distribution—which matters for real-world stability and dexterity.
Another angle: the test may not penalize unnatural poses. Diffangle-Grasp reports that their optimization improved 'naturalness' by only 2.15% over the base model [1]. That's a tiny gain, suggesting that even the improved method still produces grasps that look or feel unnatural to a human observer. Standard success metrics (e.g., does the object stay in the hand?) don't capture this, so a grasp can pass a test while being anthropomorphically odd—which is a problem for human-robot interaction or tasks requiring human-like manipulation.
The physical reality gap: penetration and displacement that tests often ignore
Even when a grasp is deemed successful, it may have physical inconsistencies that a simple test doesn't catch. Diffangle-Grasp's experiments show that their method reduced penetration volume by 11.06% compared to the base model [1]. That means the base model—and presumably many others—had non-trivial finger-object penetration, which would be physically impossible in the real world. A test that only checks whether the object is held might not penalize this, so the grasp looks fine in simulation but would fail on a real robot. The same study also mentions 'maintaining the grasping simulation displacement,' implying that displacement (how much the object moves during the grasp) is a concern [1]. If a test doesn't measure displacement, it can miss that the grasp is unstable and would drop the object in practice.
This is a known challenge in the field: the Dexterous Grasp Transformer (DGTR) paper (2024) explicitly identifies optimization challenges in the set-prediction paradigm for dexterous grasping, and they had to introduce a test-time adaptation strategy (AB-TTA) to improve grasping quality [3]. Their work suggests that even state-of-the-art models need extra steps to ensure physical plausibility, which standard tests may not enforce. So, a test that doesn't include physical metrics like penetration or displacement can give a false sense of security.
Generalization and diversity: tests that don't reveal whether the grasp will work on new objects or in new ways
Another failure mode is that a grasp synthesis method may perform well on the test objects it was trained on, but fail to generalize to new object shapes or hand configurations. The data augmentation paper (2025) highlights that generating diverse, valid grasp datasets is a bottleneck, and they propose a method to augment demonstrations using fingertip-contact-aware sampling [2]. Their experiments on YCB objects show that their method significantly outperforms existing approaches in speed and valid pose generation rate [2]. This suggests that many existing methods produce a low rate of valid grasps when faced with new objects—something a standard test on a fixed benchmark might not reveal. If a test only uses a limited set of objects, it can miss that the method doesn't generalize.
Diversity is another blind spot. DGTR (2024) emphasizes that their method predicts a diverse set of feasible grasps, and they outperform previous works in diversity metrics [3]. A test that only checks for one successful grasp per object might not penalize a method that always produces the same grasp, even if that grasp is suboptimal for certain object orientations or tasks. So, a test that doesn't measure diversity can hide that a method lacks flexibility—a key requirement for dexterous manipulation.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2023 to 2025, 4 from 2024 or later, 1 in Q1 journals — selected as the most relevant from 5 studies that passed quality screening, drawn from 39 papers retrieved from a database of over 500 million.
Sources used in this answer
Diffangle-Grasp: Dexterous Grasp Synthesis via Fine-Grained Contact Generation and Natural Pose Optimization
Diffangle-Grasp (2025) improved contact-map reconstruction error by 9.59%, naturalness by 2.15%, success rate by 3.27%, and reduced penetration volume by 11.06% compared to a base model, showing that even improved methods still have significant contact inaccuracies and unnatural poses.
Dexterous grasp data augmentation based on grasp synthesis with fingertip workspace cloud and contact-aware sampling
The 2025 data augmentation paper (FSG/AutoWS) demonstrates that existing grasp generation methods often produce low valid pose rates on new objects, and their sampling-based approach significantly outperforms prior methods in speed and valid pose generation rate on YCB objects.
Dexterous Grasp Transformer
DGTR (2024) identifies optimization challenges in set-prediction for dexterous grasping and introduces DSMT and AB-TTA to improve stability and quality; it achieves high-quality and diverse grasps on DexGraspNet, outperforming prior works in diversity metrics.
Enhancing Dexterity in Robotic Manipulation via Hierarchical Contact Exploration
HiDex (2023) presents a hierarchical planning framework using Monte-Carlo Tree Search to explore in-hand and extrinsic dexterity, solving diverse manipulation tasks across 7 hand configurations and 15 scenarios, but its focus is on planning contact sequences rather than grasp synthesis evaluation.
GrainGrasp: Dexterous Grasp Generation with Fine-grained Contact Guidance
GrainGrasp (2024) proposes fine-grained contact guidance by predicting separate contact maps for each fingertip, arguing that global contact maps are insufficient; their optimization uses only point clouds and generates precise, human-like grasps.