CoRL 2026 Workshop Will Probe Why Controllers and Hardware Beat Algorithm Tweaks in Robot Learning
pulkitology · x · 2026-09-23
younghyopark kicked off a discussion in the robot-learning community: seemingly minor system choices beneath the policy—controller tuning, hardware selection, inference infrastructure—often matter more to robot performance than architecture or algorithm changes. The thread announces the Workshop on Everything Beneath the Policy at CoRL 2026.
pulkitology amplified it, noting that policy learning depends heavily on the physical manifestation underneath what is learned, and that uncovering these factors is key to making robots work.
More from Embodied
- UW Open-Sources FrankaTwin: System ID Aligns Simulated Franka Arm to Real One Within 3.6mm — abhishekunique7 · 2026-09-23
- Qualcomm scales data-center High Bandwidth Compute memory down to phones, laptops and smart glasses — samcharrington · 2026-09-23
- Schmidhuber: superhuman physical AI will come, but not within 2 years — SchmidhuberAI · 2026-09-23
- Qualcomm CEO lays out AI smartphone vision as the hub of agentic experiences — samcharrington · 2026-09-23
- New paper: Transferring the Intelligence of VLMs to Robotic Control — _akhaliq · 2026-09-23
- Cognex Acquires RealSense for $500M, 439 Days After Intel Spinout — lukas_m_ziegler · 2026-09-23