We generate a pre-grasp pose with either the augmented Differentiable Force Closure (DFC) (A) or a diffusion model (B), then execute the grasp with a planned reaching trajectory (C) and learned lifting policy (D). We further distill the policy to a vision-based student for real-world scenarios (E).
MultiGrasp released a large-scale dataset of multi-object grasping with a Shadow Hand that includes approximately 90k grasps for 8 different objects (73.7k double-object grasps, and 16.4k single-object grasps).
MultiGrasp executed with an optimization-based motion plan to guide the hand to the pre-grasp pose, followed by an RL policy for lifting the objects.
MultiGrasp can generalize to grasping more objects. We showcase the execution trajectories of multiple cylinders, with their amount ranging from 3 to 5.
|3 Objects||4 Objects||5 Objects|
MultiGrasp generates grasp trajectories that are plausible to execute on a real Shadow Hand robot.