Kenrick Tjandra leads robotics deployment at Raise Robotics, the only U.S. company with a commercial record of autonomous robots working at the leading edge of buildings under construction. He directed deployment across 10 commercial projects in 8 states and authored the company's peer-reviewed decision framework for scaling early-stage robotics (Project Production Institute, 2026). He built the operator-training curriculum adopted by the International Union of Painters and Allied Trades and taught across 100+ training centers in the U.S. and Canada. Kenrick holds an M.S. in Mechanical Engineering from Carnegie Mellon and was an invited speaker at ICRA 2026 and Stanford.
Most autonomous systems work in the lab and stall in the field. An early-stage construction robot reaches a customer at roughly 80% of the capability that the customer needs. Whether that program crosses to 120% is not a hardware problem, it’s a leadership decision: where do you spend finite engineering capacity, and where do you build operator discipline instead? Kenrick Tjandra leads robotics deployment at Raise Robotics, which holds the U.S. commercial record for autonomous robots installing building envelopes at the leading edge of buildings under construction: 10 projects, 8 states, 4,528 robot-hours, zero safety incidents. From that record, Kenrick has built a repeatable decision framework, now peer-reviewed by Project Production Institute, which answers the question every scaling robotics team faces: “do we build the product fix, or write the operator playbook?” Kenrick will ground his talk in two failures that shaped the framework, then walk through the framework: 1) Sampling bias: your first three sites don't represent your next thirty. 2) Margin stacking: independently-defensible tolerances consume each other in series (truck → forklift → hoist → doorway → floor) until a comfortable margin is gone. 3) The framework itself: a resource taxonomy, a three-tier build-vs-workaround model, and a capability-envelope-versus-observed- variance method for anticipating where a system breaks next. Attendees leave with a decision rule for allocating engineering capacity, a vocabulary for two hidden failure modes, and the pre-mobilization gates that keep field programs from stalling; the discipline that separates a demo from a system that scales.
