Olaolu Shorinwa
Ola Shorinwa is a PhD student in Mechanical Engineering at Stanford University, where he works with Professor Mac Schwager in the Multi-Robot Systems Lab. His research focuses on distributed optimization and control algorithms for multi-agent and multi-robot systems, including distributed resource allocation and model predictive control. He is a co-author on publications such as a tutorial on distributed optimization methods for multi-robot systems and Splat-MOVER, presented at CoRL 2024.
“CLAP demonstrates that a single video world model trained across multiple robot embodiments (Franka, WidowX, bimanual YAM, G1 humanoid) can match or surpass models trained exclusively on one robot platform. On the DROID dataset, CLAP-CURR achieves a PSNR of 19.138 and LPIPS of 0.204, compared to the single-embodiment Ctrl-World baseline's 18.928 PSNR and 0.205 LPIPS”
Source→“The robotics community has largely assumed that relative action spaces (e.g., 'move 3cm in x') are easier to learn than absolute action spaces because they have narrower distributions. CLAP's experiments contradict this for end-effector-conditioned video models: 'relative-action spaces underperform absolute-action spaces in future prediction conditioned on end-effector actions across all perceptual metrics and robot environments, e.g., by about 14.6% in LPIPS in the DROID environment'”
Source→“Ola Shorinwa: Co-author whose work often focuses on multi-agent systems and optimization.”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.