Grasp Planning: Computing Stable Gripper Poses for Object Manipulation

Grasp planning is the computational process of determining a gripper pose and configuration that will result in a stable grasp on a target object. The problem is defined in the robot's configuration space: given a sensory observation of the scene (RGB image, depth map, point cloud), compute the 6-DoF pose (position and orientation) of the gripper and any gripper-specific parameters (jaw width, finger joint angles) that maximize the probability of successful object acquisition.

The classical approach to grasp planning used analytical methods based on contact mechanics. Given an object's 3D model, surface friction properties, and the gripper geometry, force closure analysis determines whether a set of contact points can resist arbitrary external wrenches. Form closure analysis is a stronger condition requiring geometric constraint alone. These methods provide theoretical guarantees but require exact object models — a condition rarely satisfied outside structured manufacturing.

Modern data-driven grasp planning replaces analytical computation with learned prediction. A neural network, trained on large datasets of grasp attempts with success/failure labels, learns to predict grasp quality directly from raw sensor observations. This approach generalizes to novel objects because the network learns geometric features (edges, surfaces, symmetries) that are predictive of grasp success across object categories. The shift from model-based to data-driven grasping has been the defining trend in manipulation research over the past decade.

For teams deploying robotic grasping systems, the choice between simulation-based and real-data-based training depends on the deployment domain. If the target objects have regular geometry and known material properties (warehouse logistics, manufacturing), simulation-based planners trained on procedurally generated objects provide excellent performance at low data cost. If the target objects are diverse and unpredictable (household environments, recycling), real-world grasp data is essential for capturing the physics and visual appearance that simulation cannot fully replicate.

The most effective practical approach combines both: pretrain on large-scale simulated grasps for broad coverage, then fine-tune on real-world grasp data from the target domain for accuracy. This sim-to-real transfer approach reduces real data requirements by 5-10x compared to training from scratch on real data.

Claru provides real-world grasp success datasets that complement simulation-generated data. Each dataset includes calibrated depth maps, 6-DoF grasp pose annotations, binary success labels, and object identity metadata. The real-world data captures sensor noise patterns, lighting variation, and physical properties that improve sim-to-real transfer for production grasp planning systems.

Claru provides real-world grasp success datasets spanning diverse object categories, with calibrated depth maps, 6-DoF grasp poses, and binary success labels. This data complements simulation-generated grasps by providing the real-world physics and sensor noise that production grasp planners need.

Grasp planning is the computational problem of determining where and how a robot gripper should contact an object to achieve a stable, functional grasp. The output is a 6-DoF gripper pose (3D position and 3D orientation) plus gripper configuration parameters (finger aperture for parallel-jaw grippers, joint angles for dexterous hands). The input is typically a visual or geometric representation of the target object and its surroundings — an RGB image, depth map, or point cloud.

Classical grasp planning methods operated on known object models. Given the exact 3D geometry, mass distribution, and friction coefficients of an object, analytical methods (force closure analysis, form closure analysis) could compute provably stable grasps. These methods worked well in structured manufacturing environments where every object was known in advance, but they could not handle novel objects with unknown geometry.

Modern data-driven grasp planning replaces analytical computation with learned prediction. Neural networks are trained on large datasets of grasp attempts — either from real robots or from physics simulation — to predict grasp success probability for candidate gripper poses. At inference time, the network evaluates thousands of candidate grasps in parallel and selects the highest-scoring one. This approach generalizes to novel objects because the network learns geometric features predictive of grasp success rather than memorizing specific object models.

The dominant paradigm in modern grasp planning is sampling-based prediction. Given a point cloud or depth image of the scene, the system samples a large set of candidate grasp poses (typically 1,000-10,000), scores each candidate using a learned quality network, and executes the highest-scoring grasp. Contact-GraspNet (Sundermeyer et al., 2021) and AnyGrasp (Fang et al., 2023) exemplify this approach, achieving above 90% grasp success rates on novel objects in cluttered scenes.

An alternative paradigm is direct grasp regression, where the network directly outputs a single grasp pose given the observation. While simpler, this approach struggles with multimodal grasp distributions — most objects have many valid grasps, and regressing to a single one can produce invalid averages. Diffusion-based grasp planners (analogous to Diffusion Policy for manipulation) address this limitation by modeling the full distribution over valid grasps.

Training data for grasp planning comes from two main sources. Simulation-based data generation uses physics engines (Isaac Gym, PyBullet, MuJoCo) to execute millions of grasp attempts on procedurally generated or scanned objects, labeling each attempt as success or failure. Real-robot data collection uses automated grasp-and-lift trials, with success determined by whether the object remains grasped after lifting. Both approaches produce (observation, grasp_pose, success_label) tuples that train the quality prediction network.

The volume of training data for grasp planning varies dramatically by approach. Simulation-based methods can generate billions of grasp attempts at low cost, with the main bottleneck being the diversity and realism of the simulated objects and physics. Real-robot methods require fewer samples (50,000-600,000 grasp attempts) but each sample is expensive, taking 5-30 seconds of robot time plus wear on hardware.

Object diversity in the training set is the strongest predictor of generalization performance. A grasp planner trained on 100 objects with 10,000 grasps each generalizes better to novel objects than one trained on 10 objects with 100,000 grasps each. The GraspNet-1Billion benchmark provides 1 billion grasp annotations across 88 objects with 190 cluttered scenes, establishing a standard for training data scale and evaluation protocol.

For teams building custom grasp planners, Claru provides real-world grasp success data across diverse object sets, with calibrated depth maps and point clouds annotated with 6-DoF grasp poses and binary success labels. This data complements simulation-generated grasps by providing real-world physics, real sensor noise, and real-world object diversity that simulation cannot fully capture.