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Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Sat Jan 24, 2026 3:37 am
by ivan22
@george92 Pretty much this. One thing to add: Remote work remains genuinely limited for most hands-on humanoid hardware roles, given the need for physical access to robots and test environments, though software-only roles (simulation, perception algorithms, offline learning) increasingly do offer remote or hybrid arrangements. RSS (Robotics: Science and Systems), ICRA, and the IEEE-RAS Humanoids conference are commonly cited as the most directly relevant venues for someone specifically interested in legged locomotion and humanoid control research, as opposed to broader AI/ML conferences.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Fri Jan 30, 2026 6:45 am
by mohammed.rossi
Can I ask a dumb follow-up - A whole-body control engineer's day-to-day work is a mix of formulating and tuning constrained optimization problems, debugging why a controller behaves differently on hardware than in simulation, and a surprising amount of time spent on numerical stability and solver performance rather than pure algorithm design. In the UK, base salaries for robotics roles reportedly range from around £50,000 for new graduates up to roughly £200,000 for senior whole-body-control or reinforcement-learning specialists - a notably wide band reflecting how specialized the top end of the field has become.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Mon Feb 02, 2026 3:42 pm
by lbianchi
Pretty much this. One thing to add: 24/7 factory-pilot support roles (the engineers keeping a deployed fleet running through real shifts) reportedly carry a real burnout risk, since they combine the unpredictability of early-stage hardware with the operational pressure of a live production environment. The common academic path into this field is a master's or conversion course in robotics or machine learning, followed by an internship or research role at a known lab or company - a PhD is common but increasingly not strictly required for industry roles, especially on the applied engineering side.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Wed Feb 04, 2026 5:43 pm
by jchen
Related question - Internship competitiveness at the well-known humanoid companies has increased sharply as the field's visibility has grown, with the applicant pool now including not just robotics students but a lot of general CS/ML students drawn in by the sector's high profile.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Sun Feb 15, 2026 9:57 pm
by jessica_faro
Here's what I know on this: A whole-body control engineer's day-to-day work is a mix of formulating and tuning constrained optimization problems, debugging why a controller behaves differently on hardware than in simulation, and a surprising amount of time spent on numerical stability and solver performance rather than pure algorithm design.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Sat Feb 21, 2026 12:43 am
by deborah59
@jessica_faro Slight correction, though the overall point stands: RSS (Robotics: Science and Systems), ICRA, and the IEEE-RAS Humanoids conference are commonly cited as the most directly relevant venues for someone specifically interested in legged locomotion and humanoid control research, as opposed to broader AI/ML conferences. Totally unrelated but has anyone else noticed how fast component costs are dropping this year.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Fri Feb 27, 2026 6:30 pm
by emma_whit
@deborah59 Can I ask a dumb follow-up - Transitioning from industrial automation into humanoid-specific roles is a increasingly common and viable path, since a lot of the underlying skills (motion control, safety systems, real-time software) transfer directly, even though the specific dynamics and learned-control components are new.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Sat Feb 28, 2026 9:36 pm
by lperez
Genuinely curious - RSS (Robotics: Science and Systems), ICRA, and the IEEE-RAS Humanoids conference are commonly cited as the most directly relevant venues for someone specifically interested in legged locomotion and humanoid control research, as opposed to broader AI/ML conferences. Transitioning from industrial automation into humanoid-specific roles is a increasingly common and viable path, since a lot of the underlying skills (motion control, safety systems, real-time software) transfer directly, even though the specific dynamics and learned-control components are new. Kind of makes me think about how different this all looked even three years ago.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Tue Mar 10, 2026 5:20 am
by zoeanderson
@lperez +1 to this. Worth adding: Transitioning from industrial automation into humanoid-specific roles is a increasingly common and viable path, since a lot of the underlying skills (motion control, safety systems, real-time software) transfer directly, even though the specific dynamics and learned-control components are new. Companies like Figure, 1X, Apptronik, and Agility Robotics are reportedly pulling senior ROS, controls, and learning engineers directly out of warehouse-automation and industrial-robotics roles - a sign of real talent competition between adjacent industries, not just fresh grads entering the field.

Re: Is it better to specialize early (perception, controls, ML) or stay generalist?

Posted: Tue Mar 10, 2026 8:19 pm
by george92
Follow-up question though - A whole-body control engineer's day-to-day work is a mix of formulating and tuning constrained optimization problems, debugging why a controller behaves differently on hardware than in simulation, and a surprising amount of time spent on numerical stability and solver performance rather than pure algorithm design. How much ML background a controls role actually requires varies a lot by company and team - some whole-body control positions remain heavily classical-optimization-focused, while others increasingly expect familiarity with reinforcement learning or imitation learning even for what used to be a purely classical-controls job.