How Firm Should a Grasp Be?

arXiv:2610.11221 · cs.RO, cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Ablation — 10× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Closed-form Continuous-time — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 2× in this paper
• AUC — 1× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• GPU-accelerated Incremental Potential Contact — 1× in this paper
• International Conference on Automation Science and Engineering — 1× in this paper
• Inverse Problem — 1× in this paper · explained in 2 episodes
• National Science Foundation — 1× in this paper
• Office of Naval Research — 1× in this paper
• Optical Flow — 1× in this paper · explained in 2 episodes
• PID Controller — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Temporal Abstraction — 1× in this paper · explained in 2 episodes

Sources

• DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
• 3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
• Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
• Towards synthesizing grasps for 3D deformable objects with physics-based simulation
• TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment
• Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation
• Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols
• ShapeNet: An Information-Rich 3D Model Repository
• SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving