SpikeGrasp: A Benchmark for 6-DoF Grasp Pose Detection from Stereo Spike Streams
This work addresses robotic grasping for dynamic objects by proposing a new paradigm that could enable more fluid and efficient manipulation, though it is incremental as it builds on neuro-inspired concepts.
The paper tackles the problem of 6-DoF grasp pose detection by introducing SpikeGrasp, a neuro-inspired framework that processes raw stereo spike streams without point cloud reconstruction, surpassing traditional baselines in cluttered and textureless scenes with notable data efficiency.
Most robotic grasping systems rely on converting sensor data into explicit 3D point clouds, which is a computational step not found in biological intelligence. This paper explores a fundamentally different, neuro-inspired paradigm for 6-DoF grasp detection. We introduce SpikeGrasp, a framework that mimics the biological visuomotor pathway, processing raw, asynchronous events from stereo spike cameras, similarly to retinas, to directly infer grasp poses. Our model fuses these stereo spike streams and uses a recurrent spiking neural network, analogous to high-level visual processing, to iteratively refine grasp hypotheses without ever reconstructing a point cloud. To validate this approach, we built a large-scale synthetic benchmark dataset. Experiments show that SpikeGrasp surpasses traditional point-cloud-based baselines, especially in cluttered and textureless scenes, and demonstrates remarkable data efficiency. By establishing the viability of this end-to-end, neuro-inspired approach, SpikeGrasp paves the way for future systems capable of the fluid and efficient manipulation seen in nature, particularly for dynamic objects.