CoDex combines VLMs, constrained optimization, and RL to autonomously discover grasp-move-actuate policies for functional manipulation of unseen objects with internal mechanisms.
Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach
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abstract
This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps at every pixel. This one-to-one mapping from a depth image overcomes limitations of current deep-learning grasping techniques by avoiding discrete sampling of grasp candidates and long computation times. Additionally, our GG-CNN is orders of magnitude smaller while detecting stable grasps with equivalent performance to current state-of-the-art techniques. The light-weight and single-pass generative nature of our GG-CNN allows for closed-loop control at up to 50Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies. In our real-world tests, we achieve an 83% grasp success rate on a set of previously unseen objects with adversarial geometry and 88% on a set of household objects that are moved during the grasp attempt. We also achieve 81% accuracy when grasping in dynamic clutter.
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UNVERDICTED 3representative citing papers
GraspFoM creates a shared 3D latent from SAM3D priors, adds an anchor-initialized diffuser for multimodal grasps, and uses reconstruction-aware scoring plus residual updates to jointly achieve SOTA reconstruction and grasping with few extra parameters.
Parametric generative model for grasp synthesis from demonstration is faster to compute and achieves at least 10% higher success rate in simulation than prior methods while supporting task constraints.
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Generative grasp synthesis from demonstration using parametric mixtures
Parametric generative model for grasp synthesis from demonstration is faster to compute and achieves at least 10% higher success rate in simulation than prior methods while supporting task constraints.