How do professors/advisors in this space typically fund PhD students right now?

[ethan_fisc]

Been lurking on this one for a while, finally decided to ask. 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. 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. Feel free to tell me I'm overthinking this.

[mohammed64]

@ethan_fisc +1 to this. Worth adding: 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.

[green28]

Minor factual note: 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. Makes me wonder how this looks in another five years.

[nicole10]

@green28 This matches something I went through recently. 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.

[nancy_lewi]

@nicole10 Appreciate the detailed answer. 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. 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.

[mary.taylor6]

@nancy_lewi I'd take that specific number with a grain of salt, honestly. Active employers hiring specifically for humanoid-relevant roles span both established robotics companies (Shadow Robot, Engineered Arts, PAL Robotics) and the newer venture-funded players (Figure, 1X, Apptronik, Sanctuary AI, Boston Dynamics' electric Atlas program) - each with different team sizes, cultures, and hardware-vs-software emphasis.

[arjunsanchez]

@mary.taylor6 Not to derail, but this reminds me of something adjacent: A commonly cited skill gap in new-grad applicants is practical systems integration experience - many candidates are individually strong in ML or in mechanical design, but comparatively few have hands-on experience getting perception, planning, and control to work together reliably on real hardware under time pressure.

[george92]

@arjunsanchez Here's the relevant bit as far as I understand it: Robotics engineers in the US were reportedly seeing $150k-$205k total comp at mid-level and $205k-$300k at senior level in 2026, with humanoid- and foundation-model-specialist roles clearing $280k-$475k - a meaningful premium over general robotics/automation roles. A field-deployment or reliability engineer's day-to-day work leans much more toward diagnosing real-world failure patterns, managing spare-parts logistics, and working directly with the customer site than toward algorithm development - a genuinely different role than a lot of new grads expect going in. Kind of makes me think about how different this all looked even three years ago.

[sharonschmidt]

This is a great summary, thanks. 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.

[jessica_faro]

@sharonschmidt To answer this directly: 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. A strong, well-documented personal project (even a modest DIY build or a solid simulation-based RL project) reportedly carries real weight in hiring for this field, partly because the field is young enough that a demonstrated hands-on track record can meaningfully substitute for a less-relevant formal credential.