Steer2Grasp: Inference-Time Embodiment-Aware Steering for Diverse Physically Feasible Grasp Diffusion
cs.RO
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- Robotic Grasping from Classical to Modern: A Survey
- DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps
- DAGDiff: Guiding Dual-Arm Grasp Diffusion to Stable and Collision-Free Grasps
- BiGraspFormer: End-to-End Bimanual Grasp Transformer
- EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
- Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control
- Diffusion Models Beat GANs on Image Synthesis
- Classifier-Free Diffusion Guidance
- Planning with Diffusion for Flexible Behavior Synthesis
- Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
- A General Framework for Inference-time Scaling and Steering of Diffusion Models
- Diffusion Predictive Control with Constraints
- Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
- Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
- cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots
- ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
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