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Research / Working with people

Are humanoid robots safe to work alongside people?

Updated November 13, 2026 · 3 papers · How we rate evidence

Where things stand

Researchers are building the pieces, but the evidence is lab-scale. A touch reflex on a Unitree G1 reacts within 10 milliseconds and halves contact impulse against a slower reflex, and a social-gesture system reached a 0.90 accuracy score over 150 real-robot trials. None of the papers we track report a safety certification or a long-term trial beside workers.

Safety work is moving below the main controller. Learned controllers typically update about 50 times a second, which is too slow to react to a hit, so teams are adding reflex paths that act directly on the motors.

Compliance, or giving way when pushed instead of resisting, is a growing focus, because a robot that yields is less likely to hurt someone it bumps into.

Social interaction research, such as handshakes and hugs, is still built on small groups of participants. A robot that is safe in a lab demo is not the same as one cleared to share a factory aisle.

The papers

Strong evidencePhysical robotUnitree G1 with BrainCo hands

AffectiveVLA: handshakes, fist bumps and hugs on cue

AffectiveVLA: Safe and Intent-Aware VLA Control for Human–Humanoid Social Interaction · Nikita Kuzmin et al. (Skolkovo Institute of Science and Technology) · arXiv 2611.07712 · Nov 5, 2026 · Preprint

What they did
Reads what a person intends from video and responds with a handshake, fist bump or hug, with a spoken safe-stop command.
Result
With intent recognition added, it scored 0.90 on a combined accuracy measure (macro-F1, 0 to 1) over 150 real-robot trials: hug 1.00, handshake 0.92, fist bump 0.78. The best model without it scored 0.75. The voice stop worked in 10 of 10 trials.
Caveat
Training data came from 19 people, and the user study on how safe and comfortable it feels is still running.

Checked against: Paper text. Results as reported by the authors; not peer-reviewed unless noted.

Strong evidencePhysical robotUnitree G1

ReflexArc: a 10-millisecond touch reflex

ReflexArc: Bottom-Up Integration of Instantaneous Tactile Reflexes into Whole-Body Humanoid Control · Mingyu Wang et al. · arXiv 2611.04424 · Nov 3, 2026 · Preprint

What they did
Gives a humanoid a fast touch reflex: skin sensors trigger a pull-away motion sent straight to the motors, skipping the slower main controller.
Result
The reflex reaches the motors within 10 ms. Against the same reflex delayed by 20 ms, the first force spike fell 34.1% and contact impulse fell from 0.263 to 0.123 N·s, about 2.15 times lower, across three kinds of hazardous contact.
Caveat
Controlled lab contact tests. This is not a safety certification.

Checked against: Abstract. Results as reported by the authors; not peer-reviewed unless noted.

Moderate evidencePhysical robot and simulationNot named in the abstract

CompliantBot: giving way when pushed

CompliantBot: Learning Whole-Body Compliance across Task Interfaces for Humanoid Control · Haoyang Li et al. · arXiv 2611.01748 · Nov 1, 2026 · Preprint

What they did
Teaches a humanoid to give way when pushed or pulled, by moving its arms, stepping or turning its body, instead of resisting.
Result
The authors report it yielded farther under steady pulls, with lower interaction forces, lower joint power and the highest task completion under strong pulls among the methods compared. Real-robot demonstrations are shown.
Caveat
The abstract gives no numbers or named baselines, and does not say which comparisons were run on hardware.

Checked against: Abstract. Results as reported by the authors; not peer-reviewed unless noted.

More research

How we pick and rate papers

  • We cover primary research that tests AI, perception, planning or control on a humanoid robot, or reports a distinct humanoid experiment. Product launches, funding news and promotional videos without a paper are left out.
  • Every paper links to its primary source. Most are arXiv preprints, which are not peer-reviewed; we say so, and add the journal or conference version when one is confirmed.
  • We check the numbers against the paper itself, and say whether we read the abstract or the paper text.
  • We separate physical-robot results from simulation, and note when a robot was teleoperated rather than acting on its own.
  • Strong means quantified tests on a physical humanoid with defined tasks and real comparisons. Moderate means limited hardware results or partial evidence. Preliminary means simulation-only or exploratory work.

Spotted an error, or a paper we should cover? Tell us, with a link to the paper.