I’m deep into a robotics project that already has the mechanical platform built and basic firmware running, but the intelligence layer and sensor pipeline still need expert hands. I’m looking for guidance on two fronts: • AI algorithm development – from choosing suitable model architectures to refining training strategies that fit on-board compute limits. • Sensor integration – aligning multi-modal data streams (vision, lidar, IMU) so the algorithms receive clean, time-synced inputs. In practical terms, I need you to review my current approach, point out bottlenecks, and outline a step-by-step implementation plan I can execute with my in-house team. Code snippets or pseudo-code that demonstrate best practices are welcome, and I’ll appreciate straight-talk on toolchains (ROS2, PyTorch, TensorRT, or any stack you swear by). Please tell me about projects where you have tackled similar AI-for-robotics challenges, the hurdles you overcame, and the measurable gains achieved. Your experience is the deciding factor, so focus there rather than lengthy proposals. Once we agree on scope, I expect: 1. A concise technical roadmap for the algorithms and sensor data flow 2. Annotated reference code or templates illustrating key sections 3. A Q&A session (recorded or written) clarifying the integration steps If this sounds like your sweet spot, let’s connect.