Md Faizal Karim supervised by Prof. K Madhava Krishna  received his Master of Science – Dual Degree  in Computational Linguistics (CLD). Here’s a summary of his research work on Physically Stable Dual-Arm Grasp Generation

Robotic manipulation of large and complex objects requires not only the ability to generate feasible grasps, but also to ensure stability under multi-contact interactions, particularly in dual-arm settings. While recent advances in data-driven grasp synthesis have shown promising results for single-arm grasping, extending these methods to dual-arm scenarios introduces additional challenges such as coordinated contact placement, force balance, and collision avoidance. This thesis addresses these challenges through a unified progression from generative modeling to analytical understanding, which are subsequently combined into an end-to-end framework for dual-arm grasp generation. We begin with Constrained GraspDiffusion Fields (CGDF), a diffusion-based generativemodelfor 6-DoF grasp synthesis on complex objects. CGDF introduces a part-guided diffusionmechanism that enables sample-efficient generation of grasps constrained to specific regions of an object, allowing dense and targeted grasp proposals even on large and geometrically intricate shapes. This provides a strong foundation for generating diverse and region-aware grasp candidates. Building on this, we recognize that dual-arm grasping requires a principled notion of stability beyond independent single-arm grasps. To address this, we developed DG16M, a large-scale dataset and an analytical framework for evaluating dual-arm grasp stability using an optimization-based force-closure formulation. By explicitly modeling contact forces, friction constraints, and external disturbances, this work provides a physically grounded metric for assessing grasp quality and enables the learning of stability-aware models. Finally, we integrate these two perspectives— generativemodeling and analytical evaluation into DAGDiff, an end-to-end diffusion-based framework for directly generating dual-arm grasp pairs. Unlike prior approaches that rely on heuristic pairing or region selection, DAGDiff formulates grasp generation in the joint SE(3)×SE(3)space and incorporates guidance from stability and collisionaware classifiers during the diffusion process. This allows the model to generate dual-arm grasps that are not only diverse, but alsoforce-closure stable and collision-free directlyfrom raw input point clouds. Together, these contributions establish a principled pipeline for dual-arm grasp synthesis: from region-aware generative modeling, to analytical stability evaluation, to fully integrated end-to-end learning. This work advances the state of robotic manipulation by enabling reliable, generalizable, and physically grounded dual-arm grasping on complex real-world objects. 

June 2026